<p>The interior permanent magnet synchronous motor (IPMSM) constitutes a high-dimensional, strongly coupled system, rendering its multi-objective optimization design a challenging problem. This paper presents a new nested hierarchical optimization strategy for multi-objective optimization of IPMSM. Initially, the Pearson correlation coefficient method and cross-factor variance analysis techniques are used to assess the relationship between design parameters and optimization objectives, allowing for the nested organization of these parameters. Second, Kriging surrogate models are constructed to replace the motor’s finite element models. Subsequently, the Red-billed Blue Magpie Optimizer (RBMO) is extended to the multi-objective domain and, after comparative testing, integrated with the surrogate model to conduct multi-objective optimization of parameters at each layer. Finally, finite element simulations are conducted to compare the performance of the motor before and after optimization. The results demonstrate that the Kriging models exhibit high accuracy and that the improved algorithm possesses robust multi-objective optimization capabilities. Furthermore, the optimized IPMSM exhibits an increase in average torque, while issues of excessive torque ripple and cogging torque are significantly mitigated. These outcomes validate the feasibility of the optimization strategy proposed in this paper.</p>