Aiming at the problem of multivariable and high nonlinearity of switched reluctance motors and the inability of traditional design process to obtain the optimal solution quickly and accurately, a parameter optimization strategy based on Kriging model and improved particle swarm algorithm is proposed. Firstly, a multi-objective optimization model is established, and Taguchi orthogonal method is used for sensitivity analysis, and the optimization variables are divided into two subspaces according to the sensitivity magnitude. Secondly, in order to improve the convergence speed and global optimization accuracy of multi-objective particle swarm optimization algorithm, the environmental induction mechanism in beetle antennae search algorithm and the crossover and mutation strategy in genetic algorithm are introduced. Finally, Kriging model is established and improved particle swarm algorithm is used to iteratively optimize the two subspace parameters. The experimental results show that the optimized torque ripple is reduced by 23% and the average torque is increased by 2. 3%, maintaining a large average torque with a significant reduction of torque ripple. The conclusion is that the combination of improved particle swarm optimization algorithm and Kriging model is suitable for optimization process of switched reluctance motor, which can significantly improve optimization efficiency and ensure solution accuracy.