This paper shows the use of Data Envelopment Analysis (DEA) to rank and select the solutions found by a Pareto Genetic Algorithm (PGA) to problems of robust design in multiresponse systems with many control and noise factors. The efficiency analysis of the solutions using DEA shows that the PGA finds a good approximation to the efficient frontier. Additionally, DEA is used to determine the combination of a given level of mean adjustment and variance in the responses of a system, so as to minimize the economic cost of achieving those two objectives. By linking that cost with other technical and/or economic considerations, the solution that best matches a predefined level of quality can be more sensibly selected.