For the influence of machining parameters on tool wear and tool life, a detection and control technology for the tool wear based on neural network and genetic algorithm was explored. The orthogonal experimental design method was used to carry out the plane-milling experiment of the martensitic stainless steel and a universal tool microscope was adopted to measure the tool flank wear to obtain training samples. And then, with the nonlinear mapping of BP neural network, the finite training samples were employed to formulate the prediction model of the tool wear for cutting speeds, feed per tooth, the depth of cut, and cutting time. Experimental results show that the prediction error of the proposed neural network model is no more than 5.4%. Finally, the optimal model of machining parameters was established with the objective of minimizing the tool wear. According to the wear of each generation tool parameter, the evaluation function was defined for the fitness of the individual and the genetic algorithm was skillfully developed to solve the optimal model of tool wear. In comparison with the Taguchi method, the optimal machining parameters obtained by the genetic algorithm based optimal model decrease the tool wear by 6.734%. The proposed method not only improves the calculation efficiency and precision, but also provides a basic theory for the selection of machining parameters.