We present a Pareto Genetic Algorithm (PGA), which finds the Pareto frontier of solutions to problems of robust design in multiobjective systems. The PGA was designed to be applied using Taguchi's Parameter Design method, which is the most frequently used approach by practitioners to executing robust design studies. We tested the PGA using data obtained from a real singleoutput system and from multiobjective process simulators with many control and noise factors. In all cases, the PGA delivered Pareto-optimal solutions that adequately achieved the objective of robust design. Additionally, the discussion of the results showed that having those Pareto solutions helps in the selection of the best ones to be implemented in the system under study, especially when the system has many control factors and responses.