Variable selection plays an important role in the quantitative analysis of near infrared spectra. The accuracy of near infrared spectroscopy can be improved by eliminating the redundant variables and selecting the characteristic variables. Competitive adaptive reweighted sampling (CARS) method is a newly developed strategy for wavelength selection by employing the principle “survival of the fittest” on which Darwin’s Evolution Theory is based. The number of selected wavelengths by CARS is much smaller than those of other methods with fast calculating speed and high accuracy. However, it is easy to get inconsistent results between the calibration and validation set due to the excessive attention on the cross validation results. In order to develop a robust variable selection method, by combining the advantages of CARS and “window”, a new tactic called window competitive adaptive reweighted sampling (WCARS) is employed to select characteristic variables and applied to the analysis of the near infrared spectra of the complex plant samples and the contents of the chemical components. Compared with the results of CARS method, accurate quantitative results can be obtained by the WCARS method. Furthermore, the results of correction set are consistent with those of the prediction set, and the problem of overfitting can be avoided. The results show that WCARS tactic can efficiently improve the accuracy and stability of variables selection and optimize the precision of prediction model, which has a certain application value.