Well designed experiments can dramatically improve the statistical accuracy of simulation output data and facilitate the statistical analysis. In practice, however, the design of experiments is the most neglected phase of a simulation study. What is needed is good, sound experimental design strategies that can be easily implemented by practitioners. In this dissertation, an experimental design strategy is proposed for finding robust system designs in discrete-event simulation, where robust system designs are system designs that are insensitive to uncontrollable system noise. A conceptual framework for automating the proposed experimental design strategy is constructed and a prototype system for testing the efficacy of the proposed strategy is developed. Concentration is on the domain area of manufacturing systems. The experimental design strategy proposed in this dissertation can be broken down into three phases. The first phase involves finding a range of feasible values for the system of design variables. This is accomplished by augmenting the simulation program with an expert system. The second phase involves constructing experimental design plans. The experimental design plans suggested are plans that allow robust system designs to be found. The third phase, the optimization phase, involves searching for a best system design, where a best system design is a system design which optimizes the performance measures of interest and is not sensitive to uncontrollable system noise. The strategy proposed is evaluated with two models of a jobshop.