As one of the key actuators in acquisition, tracking and pointing (ATP) systems, the fast steering mirror (FSM) has controller parameters that directly affect its dynamic response performance and tracking accuracy. To address the problems that fuzzy PID controller parameters are high-dimensional and strongly coupled, that manual tuning is difficult to balance rapidity and stability, and that practical hardware factors such as drive amplitude limitations, measurement noise, and vibration of flexible mechanisms further increase the difficulty of parameter tuning, this paper proposes a fuzzy PID parameter optimization method for fast steering mirror based on a multi-strategy enhanced particle swarm optimization algorithm (MEPSO). First, three strategies, namely optimized Latin hypercube sampling (OLHS), population dynamic intervention, and parameter adaptation, are introduced to improve the particle swarm optimization algorithm (PSO), thereby enhancing the initial population coverage, suppressing population diversity degradation during iteration, and balancing global exploration and local exploitation. Second, a composite cost function including rise time, settling time, overshoot, integral of time multiplied by absolute error, steady-state error, and engineering penalty terms is constructed to guide the algorithm toward control parameters satisfying drive amplitude limitations and response stability constraints. Then, benchmark function tests are conducted to verify the global optimization capability of the proposed algorithm. Multi-model simulations and FSM step response simulations are performed to evaluate the control performance of the parameter optimization method, and dSPACE hardware-in-the-loop (HIL) experiments are further carried out to verify its engineering feasibility. The dSPACE experimental results show that, under a 0.1° step command, compared with the fuzzy PID controller parameters optimized by the conventional PSO algorithm, the proposed algorithm reduces the overshoot by approximately 80.9%, shortens the settling time by approximately 31.3%, and further decreases the steady-state average deviation. The results indicate that the proposed method can improve the response smoothness, tracking accuracy, and engineering applicability of the FSM system.