In order to control the quality of laser milling layer, back propagation (BP) neural network model of the milling laser quality including milling depth and width, and milling layer parameters including laser power, laser velocity and defocus amount is set up. The weight and threshold of the BP neural network is optimized by genetic algorithm (GA), and a quality prediciton model is constructed based on BP neural network. The quality of the laser milling layer is forecasted by the model of GA-BP neural network. The results from BP neural network are compared with that of GA-BP neural network. The results of simulation show that the errors of the two network models are smaller, and the test accuracy are higher. Therefore, the two network models can be used to predict the quality of the laser milling. It is also shown that both the astringent and prediction accuracies of the GA optimized BP neural network are improved.