The purpose of this paper is to compare two optimization approaches, used for the research of optimal configurations of electrical machines. The first one uses a single multiphysical model, called “reference model,” directly used by a classical optimization routine. The second approach proposes the use of a second surrogate model in association with the previous reference model. This second configuration corresponds to the space mapping (SM) methodology. In both cases, models involved are multiphysical descriptions of the main phenomena occurring in electrical machines. They consider electrical, magnetic, thermal, and mechanical aspects. They use different modeling techniques (analytical relations, lumped parameter networks, and finite-element analyses), depending on the physics to be described. Appropriate coupling strategies between these different submodels are chosen to get the best results in the framework of the SM methods. This paper evaluates both approaches by a comparison of the corresponding computation costs, evaluated by the number of evaluations of the fine (reference) model. The paper shows that SM-like methods allow faster optimization processes and require a smaller number of evaluations (of the reference model) and lead to similar final machine designs. The use and the comparison of these two approaches is illustrated through the optimal sizing of an automotive electrical machine.