An approach to decision-based design involving Pareto sets is presented. Methods for generating Pareto sets using genetic algorithms are described. The paper presents a new fitness function which is a measure of Pareto-optimality in each generation. The methods are applied to a design example. Recommendations are made for developing tools to assist decision-makers in assimilating the information contained in the Pareto set.