This paper presents the development of a design optimization module for achieving the best performance of hydrostatic bearings. The design optimization module consists of two components: a bearing performance analysis module and an optimization module that utilizes optimization algorithms. Widely recognized global search methods, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were employed as the optimization algorithms. The design optimization problem was defined for hydrostatic bearings. Optimization design processes were carried out to improve load capacity, stiffness, and flow rate. Subsequent experimental validation was conducted through the fabrication of a practical experimental setup. The design optimization model demonstrated superior performance compared to the initial model while satisfying design conditions and constraints. This confirms the practical applicability of the design optimization module developed in this study.