Designing high-quality products and processes at low cost is an economic and technological challenge to the engineer. Recent advances in quality technology have resulted from considering the variation of a quality characteristic as well as its mean value. In the 1990s, much attention was given to the optimization of dual response systems as an important response surface methodology tool for quality improvement. The most recent articles have all relied on nonlinear programming techniques to obtain the actual solution. While these techniques certainly work, it is also possible to solve the dual response problems using the more familiar technique of multiple response optimization. We demonstrate this using the desirability function approach. The proposed approach allows to specify minimum and maximum acceptable values for each response in addition to specifying certain parameters to be chosen so that this gives more flexibility to the decision maker in exploring alternative solutions and the trade-offs between the mean and standard deviation responses. A numerical example illustrates the methodology. Comparisons with other existing methods were also discussed.