Parameter design is a method popularized by the Japanese quality expert G. Taguchi, for designing products and manufacturing processes that are robust in the face of uncontrollable variations. At the design stage, the goal of parameter design is to identify design settings that make the product performance less sensitive to the effects of manufacturing and environmental variations and deterioration. Because parameter design reduces performance variation by reducing the influence of the sources of variation rather than by controlling them, it is a cost-effective technique for improving quality. A recent study on the application of parameter design methodology for chemical processes reported that the use of Taguchi’s method was not justified and a method based on Monte Carlo simulation combined with optimization was shown to be more effective. However, this method is computationally intensive as a large number of samples are necessary to achieve the given accuracy. Additionally, determination of the number of sample runs required is based on experimentation due to a lack of systematic sampling methods. In an attempt to overcome these problems, the use of a stochastic modeling capability combined with an optimizer is presented in this paper. The objective is that of providing an effective means for application of parameter design methodologies to chemical processes using the ASPEN simulator. This implementation not only presents a generalized tool for use by chemical engineers at large but also provides systematic estimates of the number of sample runs required to attain the specified accuracy. The stochastic model employs the technique of Latin hypercube sampling instead of the traditional Monte Carlo technique and hence has a great potential to reduce the required number of samples. The methodology is illustrated via an example problem of designing a chemical process.