The development of a high-fidelity aerodynamic design optimization tool based on evolutionary algorithms for turbomachinery is attempted. A three-dimensional Navier-Stokes solver was used for aerodynamic analysis, so that flowfields would be represented accurately and so that realistic and reliable designs would be produced. For efficient and robust design optimization, the real-coded adaptive range genetic algorithm was adopted, and the computation was parallelized and performed on an SGI Origin 2000 cluster to reduce turnaround time. The aerodynamic redesign of the NASA rotor 67 blade demonstrated the superiority of the present method over the conventional design approach, increasing adiabatic efficiency by 2% over the original design. This increase is achieved not only at the design condition, but over the entire operating range. This design optimization method has proven to be suitable for parallel computing. This promising tool is shown to help turbomachinery designers to design higher-performance machines while shortening the design cycle and reducing design costs.