In order to improve the operating efficiency of the surface water source heat pump regional energy system in the central area of Xiangtan city, a system optimization scheduling model is established, and a neighborhood adaptive particle swarm optimization algorithm (NAPSO) is proposed to solve the constructed model. Aiming at the case where infeasible solutions may appear during the operation, a method is proposed to adjust the infeasible solutions to feasible solutions. The experimental results show that during the summer and winter, in comparison to the current scheduling method, the NAPSO algorithm can save 5.14\% and 4.50\% of the operating cost per day, respectively. Compared with the other five algorithms, the NAPSO algorithm can save up to 0.57\% and 0.50\% of the operating cost per day, respectively. The simulation experiment results show that the proposed algorithm is an effective method to solve the optimal scheduling of surface water source heat pump units.