Co-generation systems (CGS) and energy storage equipment, such as batteries and thermal energy storage (TES), have become increasingly important recently for improving energy efficiency and for adjusting or reducing peak loads of energy systems. However, optimization of operating schedules of such energy storage equipment is difficult due to its parameter dependency. In addition, a nonlinear characteristics of recent heat-source machines have made the problem more difficult. Therefore, we applied an efficient optimization method, εDE (epsilon constrained differential evolution) for single objective optimization, to minimize operating costs. Moreover, we proposed εMODE (epsilon constrained multi-objective differential evolution) to solve the complex trade-off problem (costs vs primaryenergy consumption). Each result showed that these methods could provide the optimum solution in a practical time, even if the problem had a lot of decision variables that were nonlinear. In particular, εMODE could carry out a lot of non-dominated solutions without concentrating on a certain position and generating dominated solutions.