Exergy-based analysis and optimization has been successfully used to design a variety of thermal systems to achieve greater efficiency. However, the advantages afforded by exergy destruction minimization (EDM) at the design stage have not been translated to closed-loop operation of thermal systems such as vapor compression systems (VCSs). Through online optimization and control, VCSs can effectively respond to disturbances, such as weather or varying loads that cannot be accounted for at the design stage, while simultaneously maximizing system efficiency. Furthermore, in applications where VCSs encounter high frequency disturbances, such as in refrigerated transport applications or passenger vehicles, optimizing efficiency at steady-state conditions alone may not lead to significant reductions in energy consumption. In this paper we design the first exergetic, or second law, optimal controller for a canonical four-component vapor compression system (VCS). A lumped parameter moving boundary modeling framework is used to model the two heat exchangers in the VCS. A model predictive controller is then designed and implemented in simulation using a dynamic exergy-based objective function to determine the optimal control actions for the VCS to maximize exergetic efficiency while achieving a desired cooling capacity. Simulation results show that an exergy-based model predictive controller minimizing exergy destruction achieves over 40% greater exergetic efficiency during operation than a comparable first law MPC. Moreover, the distribution of exergy destruction across individual components offers new insight into the effect of variable-speed actuators on system efficiency in VCSs. (C) 2014 Elsevier Ltd. All rights reserved.