Abstract: Electro-hydrostatic actuators (EHA) exhibit nonlinear and time-varying characteristics, which limit the effectiveness of conventional PI controllers. To enhance displacement control accuracy and dynamic response, a PI control strategy optimized by an improved genetic algorithm (IGA) was proposed. The selection, crossover, and mutation operations of the genetic algorithm were refined to strengthen global search capability and accelerate convergence, thereby optimizing the proportional gain K_p and integral gain K_i of the PI controller. Based on an established mathematical model of the EHA system, simulation analysis was conducted. Performance was compared with empirically tuned PI controllers, as well as controllers optimized using particle swarm optimization (PSO) and moth-flame optimization (MFO) algorithms. Results show that the IGA-optimized controller achieves higher control precision, faster response, and improved disturbance rejection. Compared with PSO and MFO, the IGA method reduces the system settling time by 79.6% and 72.2%, respectively. This approach provides an effective solution for high-precision EHA control and demonstrates promising theoretical and practical application prospects.