This study presents a model predictive control method based on mixed-integer linear programming， taking the chilled water storage cooling system of a data center in Guangzhou as the research object. The optimization objective of the method is to minimize the energy consumption of the cooling system. By modeling the cooling system and environmental conditions and considering energy costs and cooling system efficiency， the optimal operation strategy for chillers and the scheduling arrangement for the chilled water storage cooling system are determined. During the optimization process， this research takes into account the influence of the minimum continuous operation time of chillers on the energy consumption of the cooling system and determines the optimal value to improve stability and reduce energy waste caused by frequent chiller start-ups and shutdowns. Through an annual simulation， this method reduces the total energy consumption by 6.52% and the total operating cost by 6.93%， compared to the baseline strategy.