Finish machining of hardened steel is receiving increasing attention as an alternative to the grinding process, because it offers comparable part finish, lower production cost, shorter cycle time, fewer process steps, higher flexibility and the elimination of environmentally hazardous cutting fluids. In order to demonstrate its economic viability, it is of particular importance to enable critical hard turning processes to run in optimal conditions based on specified objectives and practical constraints. In this dissertation, a scientific and systematic methodology to design the optimal tool geometry and cutting conditions is developed. First, a systematic evolutionary algorithm is elaborated as its optimization block in the areas of: problem representation; selection scheme; genetic operators for integer, discrete and continuous design variables; constraint handling and population initialization. Secondly, models to predict process thermal, forces/stresses, tool wear and surface integrity are addressed. And then hard turning process planning and optimization are implemented and experimentally validated. Finally, an intelligent advisory system for hard turning technology by integrating experimental, numerical and analytical knowledge into one system with user friendly interface is presented. The work of this dissertation improves the state of the art in making tooling solution and process planning decisions for hard turning processes.