This paper describes a fast, efficient, robust, and automated design method used to aerodynamically optimize 3D gas turbine blade shapes implementing artificial intelligence. The design objectives are maximizing the aerodynamic efficiency and torque so as to reduce the weight and size and cost of the gas turbine engine. The procedure described here will allow a rapid, practical and low cost design that will answer the need of gas turbine industry. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. A genetic algorithm is used for global optimization purposes. One of the test cases is selected as the baseline and is modified by the design process. It was found that the efficiency can be improved from 83.9% to 85.9%, and the torque can be improved as much as 7.6%. The flow field investigations indicate enhanced secondary flow characteristics of the blade passage.