This paper presents a novel method of using Interactive Evolutionary Computation (IEC) for the design of Microelectromechanical Systems (MEMS). A key limitation of IEC is human fatigue. Based on the results of a study of a previous IEC MEMS tool, an alternate form that requires less human interaction is presented. The method is applied on top of a conventional multi-objective genetic algorithm, with the human in a supervisory role, providing evaluation only every nth-generation. Human interaction is applied to the evolution process by means of Pareto-rank shifting, which is used for the fitness calculation used in selection. Results of a test of 13 users shows that this IEC method can produce statistically significant better MEMS resonators than non-interactive evolutionary synthesis.