This paper presents a model-based supervisory and optimal control strategy for central chiller plants to enhance their energy efficiency and control performance. The optimal strategy is formulated using simplified models of major components and the genetic algorithm (GA). The simplified models are used as the performance predictors to estimate the system energy performance and response to the changes of control settings and working conditions. Since the accuracy of the models has significant impacts on the overall prediction results, the models used are linear in the parameters and the recursive least squares (RLS) estimation technique with exponential forgetting is used to identify and update the model parameters online. That is to ensure that the linear models can provide reliable and accurate estimates when working condition changes. The GA, as a global optimization tool, is used to solve the optimization problem and search for globally optimal control settings. The performance of this strategy is tested and evaluated in a simulated virtual system representing the actual central chiller plant in a super high-rise building under various working conditions. The results showed that this strategy can save about 0.73–2.55% daily energy of the system studied, as compared to a reference strategy using conventional settings.