To reduce the running energy consumption of the central air conditioner and stabilize and control the return temperature of chilled water effectively, an optimal control method of return water temperature was proposed, and the actual load demand was judged according to the deviation between the measured value of return water temperature and the set value. Firstly, the inertia weight of Particle Swarm Optimization (PSO) algorithm was made decline exponentially which made updating speed of particles match each stage of optimization process. Then, aiming at uncertain disturbance of parameters of the model, the thoughts of Artificial Immune (AI) algorithm were introduced in Particle Swarm Optimization (PSO) algorithm to form AI-PSO algorithm which could expand the diversity of particles and enforce their ability to get rid of local optimum. Finally, three parameters of Proportional Integral Differential (PID) controller were optimized with AI-PSO algorithm, and through this controller, the frequency of chilled water pump was adjusted to make return water temperature steady near set value. The experimental results show that the proposed strategy can reduce operating frequency of chilled water pump more effectively while meeting indoor load demand, in addition, energy saving effect and control quality are much better.