Abstract This experimental study focuses on high performance cryogenic machining of porous tungsten, which is classified as a difficult-to-machine material, where the quality of the machined surface porosity is one of the most important objectives. For achieving the required post‐machining porosity and surface roughness, the optimum machining parameters and tool grade, as well as cryogenic machining method, an alternative to conventional machining, were chosen. For smearing evaluation, pores on the machined surface are individually analyzed from SEM pictures. Different tool grades (uncoated carbide, ceramic, polycrystalline diamond and cubic boron nitride) are analyzed in this study. A precise correlation between the performance measures and the machining parameters, including tool grade, is developed to achieve the required performance measures. Surface roughness, porosity, tool-wear and cutting forces are measured and analyzed. A performance-based multi-objective optimization model is developed based on genetic algorithms (GA) and is used to predict the optimal cutting parameters for achieving improved machining performance.