Turbomachinery design is an iterative process that can be time-consuming and expensive,especially when an extensive knowledge of the performance envelope is required. The approach describedin the present paper can significantly cut the turnaround times down without jeopardizing the accuracy ofthe final result. A parameterization technique based on radial basis functions (RBF) is used and ReynoldsAveraged Navier-Stokes (RANS) simulations are subsequently performed on a set of selected morphedmeshes, the goal of which is to produce an aerodynamic database containing first-order, second-order andsecond-order cross derivatives of objectives with respect to parameters. New solutions, corresponding toany variations of the selected parameters, can thus be extrapolated thanks to the information included inthe aforementioned database. In this way, a meta-model is built and can be easily explored by a geneticalgorithm. This approach has been experimented on a new concept of engine cooling fan featuring lowtorque and high efficiency. A reference fan design has been adapted for the particular surrounding ofthe vehicle underhood, where the downstream flow is radially deviated from its axis by the engine. Theoptimization process has resulted in an efficiency improvement of three points for one of the obtained optima