Abstract: In view of the micro-milling tool wear exist in the process of radial tire mold side plate and the problem of high energy consumption, this paper proposes a parallel GABP neural network and the NSGA-Ⅱ multiobjective process parameters optimization met-hod. The traditional multi-target GABP prediction model was improved, and the parallel GABP neural network prediction model with three cutting elements as input, and tool wear and specific cutting energy as output was established with the experimental data as the sample. The prediction error of tool wear was reduced by 40.82%. With the minimum amount of tool wear, the minimum specific cutting energy than for the optimization goal, using the NSGA-Ⅱ genetic algorithm to multiobjective optimization of cutting parameters, 20 pateto solution were obtained. Finally, under the condition of both tool wear and specific cutting energy, the optimal cutting parameter combination was obtained through grey correlation analysis of original test data and pareto solution set: n=19 185.423 r/min, f_z=0.038 mm/z, a_p=0.517 mm, realizing process parameter optimization.