Low specific speed centrifugal pumps (LSSCP) are widely utilized in district energy systems to promote the integration of renewable energy. However, the performance of LSSCP becomes inefficient due to harsh operating conditions resulting in substantial increase in energy consumption. Many-objective optimization is significant in improving the performance of LSSCP and promoting the sustainability of district energy systems. Among the existing optimization methods, global optimization methods are limited by high computational cost when solving many-objective optimization problems, and gradient-based optimization methods face difficulties in locating the global optimum. In the present study, a hybrid optimization method was developed for solving many-objective optimization problems of LSSCP. The LSSCP optimization result of the hybrid algorithm was compared with that of the non-dominated sorting genetic algorithm (NSGA), so as to demonstrate the capacity of the proposed method. In the designed flow condition without cavitation, the hydraulic efficiency obtained by the hybrid optimization algorithm was found to be 9.5%, 5.4%, and 4.7% higher than those of the original, NSGA-II, and NSGA-III optimized results, respectively. The shaft power was 10.3%, 8.7% and 5.1% less than said three optimized results. The maximum turbulent kinetic energy in the flow passage obtained from the hybrid optimization was only 2.2 J/kg, which was 67% and 46% less than that of the NSGA-II and NSGA-III optimized results, respectively. In the designed flow condition with cavitation, the net positive suction head critical optimized by the hybrid model was 0.857 m, which was substantially reduced compared with the original and NSGA- II optimized results.