In an industry Internal Grinding is one of the most important machining processes. In this process surface quality is most expected outcome to obtain the better result. Due to complexity and nonlinearity optimization of grinding process is a challenging task. From the literature, it was noted that many of the researchers were focused on only external surfaces of a component for improving its surface quality. It is identified that very few papers deliberated the characteristics of internal grinding process which indicates that, there is a considerable research potential in improving the product quality and process performance of internal grinding. This paper aims to optimize the internal grinding process parameters to increase the expected surface quality and process performance. Similar to external surface grinding process, internal surface grinding is also influenced by parameters such as cutting speed, feed and depth of cut. Considering the variance in optimized results, different samples of EN31 steel are taken into this experimental study. From the literature, it is identified that commonly used engineering materials in many engineering applications are EN 31 Steel. The various machining parameters such as feed, cutting speed, depth of cut were monitored to analyze the process responses such as surface quality. The effect of input and output parameters was described through the ‘‘signal to noise ratio” (SNR) and ‘‘analysis of variance (ANOVA)” using Minitab 18 software. The machining parameters were optimized through Taguchi method and Genetic Algorithm tools. By analyzing the final results, optimum working conditions are recommended to improved surface quality with respect to selected input process parameters.