In view of an improvement of the system driving performance of Switched Reluctance Motors(SRM), a multi-objective optimization design method has thus been proposed with a combination of a genetic algorithm(GA)optimized back propagation(BP) neural network and non-dominated sorting genetic algorithm II (NSGA-II), so as to reduce its torque ripple and improve its average torque and efficiency. Based on a sensitivity analysis, three ontology parameters (turns, rotor pole arc coefficient, air gap) and two control parameters (turn on angle, turn off angle), which have a significant impact on the optimization objectives of SRM (switched reluctance motors), are selected as decision variables, followed by an application of the finite element analysis, GA-BP modeling, and NSGA-II algorithm for a multi-objective optimization to obtain the optimal solution.