This article presents a novel multiobjective system level optimization method to achieve the best performance of switched reluctance motor (SRM) drive systems. First, the multiobjective optimization problem for the SRM drive systems is defined. Then, all parameters of the drive systems, including the motor level and control level, are divided into three subspaces according to their influences on the objectives. Finally, the optimization of each subspace is performed sequentially until a convergence criterion is met. Then, the optimal solution can be chosen from the Pareto solutions according to a selection criterion. Meanwhile, the sensitivity analysis, the approximate models, and the genetic algorithm are employed to reduce the computation cost. To verify the effectiveness of the proposed method, an SRM drive system with a segmented-rotor SRM (SSRM) and the angle position control method is investigated. This is a high-dimensional system level optimization problem with ten parameters. The finite-element model (FEM) results are verified by the experiment results. The optimal solution has been listed and verified by the FEM. From the discussion, it can be found that the proposed optimization method is efficient and optimized SSRM drive system has high efficiency and low torque ripple.