Improving the overall performance of combined cooling, heating, and power (CCHP) systems have received great attention from both industry and academia. Due to the high amount of nonlinearities involved in detailed CCHP operations, it has to be formulated as a complex nonlinear programming (NLP) model. The existing work usually uses stochastic algorithms, such as the Genetic Algorithm (GA), which in general have difficulty obeying equality constraints for solving large-scale NLP problems. This paper presents an effective deterministic algorithm to satisfy all equality constraints and find optimal solutions in acceptable computation time. A case study has been carried out to compare our method with GA. The solution given by GA has large deviations in the constraints of cooling. Our optimal solution based on the deterministic algorithm can achieve the same energy-saving but satisfies all operational constraints, which demonstrated the validity and efficiency of our proposed method.