In order to reduce the operating costs of distribution networks with distributed energy sources and ensure power supply quality, this paper investigates the distribution network reconfiguration method and proposes a Quantum Multi-objective Artificial Rabbit Optimization Algorithm (QMOARO) to solve the problem. A dual-objective model is constructed based on active network loss and voltage deviation, and the QMOARO algorithm is applied for dynamic network reconstruction. The QMOARO algorithm uses a quantum-inspired good point set to initialize the population, enhancing its exploration capability and addressing the challenge of insufficient search space coverage. Furthermore, a two-stage quantum mutation strategy is incorporated to improve both solution diversity and global search capability. The proposed method is validated through reconstruction experiments conducted on a standard distribution network system with distributed generation. Experimental results confirm the method's effectiveness in addressing the dynamic reconstruction problem in multi-objective distribution networks, providing a practical solution for optimizing operational costs and power supply quality.