This paper presents an optimization strategy for optimal control and operation of building central chilling systems in order to minimize their energy consumption and provide improved control performance. The strategy is formulated using a systematic approach, in which the characteristics and interactions among the components and subsystems in the central chilling system are considered. The simplified models of major components are used in the strategy as the performance predictors to estimate the system energy performance and responses to the changes of control settings and working conditions. To ensure the models to provide reliable estimates when the working condition changes, the model parameters are updated online using the recursive least squares (RLS) estimation technique. A genetic algorithm (GA) is used to solve the optimization problem and search for globally optimal control settings on the basis of a cost function estimator defined. The performance of this strategy was tested and evaluated in a simulated virtual system representing the complex central chilling system in a super high-rise building under various working conditions. The model parameter identification and performance validation as well as the performance evaluation of the optimization strategy are presented.