As a relatively high energy-consuming in China, the chemical process is important and has potential to be optimized for energy saving, economic benefits and environmental protection. Multiple goals can be coordinated using multi-objective optimization algorithms. In order to improve the exploration and exploitation abilities in optimizing multiple objectives, a constrained competing evolutionary membrane algorithm is proposed. The proposed algorithm takes advantages of the distributed and parallel computing mode of the membrane computing. Populations are evolved independently in each membrane and share information between membranes based on competing communication rules. Meanwhile, the skin membrane archives global elitist solutions and serves as guidance for inner evolution processes. Finally, the optimization experiments on the ethylene cracking process, as an important production of complex chemical processes, prove that the proposed algorithm can provide enough selection for decision makers with well-distributed and converged candidate solutions. Furthermore, the solutions lead the ethylene cracking process to reach the coordinated optimum ethylene or propylene production, oil consumption reduction and carbon dioxide emission reduction. In average, the optimization solutions bring about reduction of 2697.58 tons of feed oil and 8281.57 tons of carbon dioxide emission.