With the accelerated global transition toward renewable energy sources, the penetration of distributed photovoltaic (PV) generation in distribution networks has increased dramatically. However, large-scale integration of distributed PV in traditional distribution networks presents operational challenges, particularly in voltage regulation and system stability. This paper addresses the voltage quality issues caused by distributed PV integration by establishing a multi-objective optimization mathematical model with voltage deviation minimization as the core objective. An improved hybrid intelligent optimization strategy that integrates genetic algorithm (GA), particle swarm optimization (PSO), and non-dominated sorting genetic algorithm II (NSGA-II) is proposed to systematically determine the optimal placement and capacity allocation of distributed PV units. The method combines a computationally efficient simplified voltage drop calculation model with detailed power flow analysis to enable efficient evaluation among numerous potential configurations. Comprehensive simulation studies based on the IEEE 33-node distribution system validate the effectiveness of the proposed method. Results demonstrate that the hybrid optimization algorithm significantly outperforms traditional single algorithms in terms of convergence, solution quality, and computational efficiency. Under different PV penetration scenarios, the system voltage deviation can be reduced by 23.4%–42.1%, network losses decreased by 18.7%–32.8%, and voltage stability margin improved by 12.3%–28.4%. Sensitivity analysis results further verify the robustness of the method against load level variations, PV output fluctuations, and network parameter perturbations, providing important theoretical foundation and technical support for engineering applications of distributed PV in distribution networks.