[Objective] This study investigates the multi-objective optimization of proportional integral (PI) controller parameters for the unified power quality conditioner (UPQC) using the non-dominated sorting genetic algorithm II (NSGA-II). UPQC is a crucial power quality enhancement device capable of effectively mitigating voltage fluctuations, harmonics, and imbalances in the grid. Its performance is highly dependent on the optimal configuration of controller parameters. Traditional optimization methods fail to satisfy the system’s multi-objective performance requirements and are susceptible to local optima. To overcome these challenges, this paper introduces a multi-objective optimization approach based on NSGA-II, aiming to identify a controller parameter configuration that concurrently optimizes harmonic suppression, voltage stability, and dynamic response speed. [Methods] The study utilizes NSGA-II for multi-objective optimization. This algorithm achieves global optimization of the multi-objective function through fast non-dominated sorting and crowding degree calculation. NSGA-II possesses strong global search capabilities and rapid convergence characteristics, enabling it to swiftly and accurately identify the optimal solution for UPQC controller parameter optimization. In the optimization process, harmonic suppression, voltage stability, and dynamic response speed are prioritized as the main optimization objectives. By precisely adjusting the PI controller parameters, the optimal control strategy is derived. [Results] The effectiveness and accuracy of the proposed strategy are verified through grid voltage compensation simulation and DC/AC side voltage simulation. In the grid voltage compensation simulation, the proposed strategy is compared with the nonlinear proportional integral-model predictive control (PI-MPC) strategy. The proposed strategy compensates the voltage waveform to be closer to a sine wave, with a smoother and more uniform waveform, and lower harmonic content compared to the nonlinear PI-MPC strategy. In the DC/AC side voltage simulation, the proposed strategy achieves shorter adjustment time, lower overshoot, and quicker recovery time when the system is disturbed, exhibiting stronger robustness than other strategies. [Conclusion] The PI controller parameter optimization strategy based on NSGA-II can effectively enhance the performance of UPQC under complex operating conditions, improving the system’s power quality and response efficiency. Compared to traditional methods, this optimization strategy not only improves power quality but also demonstrates better stability and faster adjustment capabilities in dynamic response processes.