To reduce the length of the superconducting wire of the superconducting motor, the no-load back EMF E and the magnetic field component perpendicular to the wire B⊥ were selected as the optimization goals. Therefore, a multi-objective optimization method based on the Response Surface Method (RSM) and Genetic Algorithm (GA) was proposed by taking a high-temperature superconducting motor (HTSM) with a rated power of 40 MW as the research object. Firstly, the overall structure, mathematical model, and output characteristics of HTSM were introduced. Secondly, the sample points required for constructing the RSM equation were obtained by Finite Element Analysis (FEA), and the multi-objective optimization of the RSM equations was performed by applying GA to obtain the Pareto optimal frontier. Finally, the calculated optimal design was simulated and verified. Compared with the initial design, the E of the optimized HTSM was increased by 4.47%, and the B⊥ was reduced by 4.83%. Based on the motor parameters obtained by multi-objective optimization, the final optimized design of HTSM was obtained. The length of the superconducting wire used in the motor has been reduced by 10.2%, which greatly reduces the cost.