Since the pioneer work on Evolution Strategies, experiment-based optimization is one of the most promising applications of evolutionary computation. Recent progress in automatic control and instrumentation provides us with a smart environment called Hardware in the Loop simulation (HILS) for such applications. However, since optimization through experiment has the severe condition of limited evaluation time and fluctuation of observations, we must develop methodologies that overcome these problems. This paper discusses the application of Multi-Objeclive Evolutionary Algorithms (MOEAs) to Experiment-Based Multi-Objective Optimization of the parameters of dynamical systems. In such applications, we must apply various parameter candidates spreading near the Pareto frontier to the system, which causes fluctuation of the observed criteria due to the transient response by parameter switching. For reduction of loss time caused by such transient responses in the evaluation of criteria, we propose the techniques called Evaluation Order Scheduling and Evaluation Time Scheduling. Numerical experiments using a formal test problem and experiments in a HILS environment with real internal-combustion engines have demonstrated the effectiveness of the proposed methods. Q 20 10 Wiley Periodicals, Inc. Electron Comm Jpn, 93(2): 12-24, 2010; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/ecj.10224