When the light passes through a scattering medium, the wavefront of the beam is disturbed due to the multiple scattering phenomena. If the light is coherent, scattered light from different optical paths will interfere, resulting in random speckles, which causes extreme degradation in imaging quality. Using wavefront shaping techniques to focus light through strongly scattering media is significant for optical microscopic active imaging through biological tissues. However, the feedback-based optimized wavefront shaping often ignores the background noise light when focusing, and even in some cases, the light intensity of a background bright spot can even exceed 1/3 of the target point. To solve this problem, this paper proposes a different iterative method from the previous multi-objective optimization genetic algorithm aiming to suppress the background noise.The effect of the population scale of the genetic algorithm and the number of modulation units of the spatial light modulator on the focusing intensity and convergence speed are systematically studied. The experimental results show that the genetic algorithm can converge quickly when the population scale and the number of modulation units reach 32 and 16 × 16, respectively. The maximal light intensity of the target region can reach 250 or more. In terms of convergence speed, the fastest convergence is achieved for a population scale of 48, but the shortest iteration time is achieved for a population scale of 32. All target intensities can achieve convergence after 60 iterations when the number of modulation units exceeds 16 × 16, and the number of modulation units is positively correlated with the iteration time. Considering above aspects, the best focusing performance of the genetic algorithm is achieved when the population scale and the number of modulation units reach 32 and 16×16, respectively.After determining the optimal experimental conditions, the study of suppressing background noise is conducted. The experiments aim to enhance the signal-to-noise ratio and propose a different iterative method from the previous multi-objective optimization genetic algorithm. The feedback function is changed to the ratio of target intensity to average background noise. Hence, iterative calculation aims to increase target intensity and suppress background noise simultaneously. The results show that the optimized algorithm significantly reduces the light intensity in the background region after the modulated area is increased. And its optimal value is basically equal to the system noise of the camera, indicating that the optimization function can suppress the light field noise in the modulated background region to the lowest level. In terms of suppressing the area of significant noise bright spot, the area of the significant noise bright spot formed with the optimized algorithm in the background region decreased by 70.4% compared with that before optimization, indicating that the optimization function can effectively suppress the local noise bright spot. In addition, the quality of the focused spot is evaluated by extracting the contour features of the target region. After obtaining the contour features, the circularities of the initial spot and the optimized spot are calculated separately using the circularity formula. The circularity of the initial spot is 0.83, while the average circularity of the optimized spot is 0.9, indicating that the shape of the optimized focused spot is closer to the ideal circular spot. Finally, a quantitative correlation model between the background average light intensity and the modulated area is proposed based on the experimental results. The experimental data are fitted well using the quantitative model, and the fitting results yield a saturated average light intensity of 246.6 in the focused region, which is consistent with the measurement results of previous experiments. It shows that the correlation model proposed in this paper can not only describe the correlation between the modulated background area and the background mean light intensity well, but also quantify the relationship between the light intensity of the focused area and the modulated background area.