PurposeThe design of electrical machines includes the computation of several requirements and, in general, the improvement of one requirement implies in a degradation of another one: this is a typical multi‐objective scenario. The paper focuses on the multi‐optimization analysis of a special switched reluctance motor.Design/methodology/approachTwo design requirements were analyzed: the average torque and the ripple torque. The electromagnetic field computation was performed by the finite element method and the torque was computed by the Coulomb's Virtual Work for several positions. This allows us to calculate the average torque and the ripple torque. Three different methods were used to obtain the Pareto set: a min‐max approach, the non‐dominated sorting genetic algorithm (NSGA) and the strength Pareto evolutionary algorithm (SPEA). In order to save the computation time, the objective functions (the average torque and the ripple torque) were replaced with surrogate functions. Kriging models were used as surrogate functions.FindingsThe evolutionary methods (NSGA and SPEA) have a similar performance. The min‐max has not the same performance. It could have the same performance only if some unconstrained optimization problems are solved before the multi‐objective optimization. The maximum relative deviation between the approximated function (Kriging model) and the same value calculated by the finite element method was equal to 0.8 percent for the average torque and 1.2 percent for the ripple torque. The ripple torque, considered as the difference between the maximum and the minimum values in the 0‐90° region, has reduced while its frequency has doubled. This last characteristic provides a better mechanical stability for the driven load because its inertia softens the ripple effects at the double the frequency. The optimized prototype presents higher torques in the region θ<0° and this allows the electronic drive to switch in a broader range rendering the motor operation more flexible.Originality/valueThe use of surrogate functions save the computation time with high accuracy. This is very important on the design of electrical machines, a typical multi‐objective scenario. Evolutionary methods seem to be well suited to solve this class of problem.