In order to solve the problems of high energy consumption and poor surface quality in the processing of radial tire mold side plate, the micromilling tests were carried out with 45 steel radial tire mold side plate as the research object. The influences of three cutting parameters, namely spindle speed, feed per tooth and cutting depth, on the specific cutting energy and surface roughness were emphatically studied. The multitarget BP neural network based on genetic algorithm was trained and detected by using experimental data samples, and the multitarget prediction of specific energy and surface roughness under different cutting parameter combinations were realized; by using the NSGA-Ⅱ,the cutting parameters were optimized with multiple objectives, and 20 pateto solution sets were obtained. The prediction and optimization results show that increasing spindle speed is beneficial to both reducing specific cutting energy and improving surface roughness, while increasing feed per tooth and cutting depth can reduce specific cutting energy, but can increase surface roughness; specific cutting energy and surface roughness are mutually inhibited and cannot be improved simultaneously. In the case of considering specific cutting energy and surface roughness, the optimal parameters are spindle speed 19 370~20 000 r/min, feed per tooth 0.055~0.06 mm/tooth and cutting depth 0.4~0.456 mm.