Traditional aerodynamic shape optimization has focused on obtaining the best design given the requirements and flow conditions. However, the flow conditions are subject to change during operation. It is important to maintain near-optimal performance levels at these off-design operating conditions. Additionally the accuracy to which the optimal shape is manufactured depends on the available manufacturing technology and other factors such as manufacturing cost. It is imperative that the performance of the optimal design is retained when the component shape differs from the optimal shape because of manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to various perturbations is built into the design optimization procedure. The imposition of this additional requirement of robustness results in a multipleobjective optimization problem requiring appropriate solution procedures. Evolutionary algorithms have been used successfully in design optimization. Here a new evolutionary method for multiple-objective optimization is presented. It draws upon ideas from several genetic algorithms and evolutionary methods; one of them being a relatively new member to the general class of evolutionary methods called differential evolution. The capabilities of the evolutionary method developed here are investigated using some complex model problems. Good solution accuracy and diversity are obtained in all these cases. The method is then applied to robust optimal design. Applications include the design of fins used in boiling heat transfer and airfoil design.