Abstract— During the manufacturing process, high productivity and good quality are desired by every manufacturer. In this paper, the Response Surface Methodology (RSM) has been applied to optimize the surface roughness (Ra) and material removal rate (MMR) when milling hardened SKD 11 steel under nano-fluid MQL condition. The three cutting parameters including cutting speed, feed rate, and depth of cut were analyzed along with the hardness of the work-piece in order to build an empirical model that could predict the surface roughness as well as the material removal rate, hence easy to determine the optimum values of Ra and MMR. Experiments were conducted using the L27 orthogonal array of DOE method developed by G. Taguchi from three levels of four input factors above. Further analysis of variance (ANOVA) was used to evaluate the reliability of the method. Under optimal condition, Ra value is 0.249 μm and the MMR value is 1498.09 mm3/min. In addition, the feed rate was identified as the most influential factor on surface roughness, followed by the depth of cut.