Due to the high-energy consumption of chilled water system in the central air-conditioning and the difficulty in dynamic adjustment for system equipment parameters with load changes, an adaptive parallel artificial immune algorithm combined with exhaustive method (EM-APAIA) was proposed to optimize equipment operating parameters under different loads, so as to reduce the operation energy consumption of chilled water system. First, the power consumption model of each piece of equipment in the system was established, and the minimum power consumption of all the equipment was taken as the optimal control objective of the chilled water system. Then, EM-APAIA was used to optimize the operation parameters of the chilled water supply temperature, the number of chilled water pumps, and the speed ratio. In the algorithm, the initialization method, migration operator, and mutation probability were improved, and the exhaustive method mechanism was introduced, enhancing its ability to optimize the equipment operating parameters for the chilled water system. Finally, a simulation experiment was carried out on an actual chilled water system of central air-conditioning. Results show that compared with the conventional setting, the total energy consumption of the system was reduced by 14.8% after its equipment operating parameters were optimized by EM-APAIA. The algorithm not only achieved better control strategy than the comparison algorithms, but also exhibited fast convergence speed and strong stability, which can be better applied to the control optimization of the equipment in central air-conditioning chilled water system.