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Wire Electrical Discharge Machining (WEDM) is widely used for machining conductive materials which are of great importance in several industrial applications. In this work, process variables optimization of Powder Mixed Wire Electrical Discharge Machining (PMWEDM) of AISI 304 stainless steel (SS) is studied using molybdenum wire as the tool material. This work illustrate the implementation of Taguchi technique and Genetic Algorithm (GA) to identify the optimal process variables of WEDM using dielectric medium mixed with Silicon Carbide(SiC) powder. Selection of optimum process variables for obtaining higher cutting efficiency and accuracy is a difficult task in WEDM due to presence of large number of control variables and complicated stochastic process mechanisms. In general, there is no perfect combination that can simultaneously result in both the maximum material removal rate (MRR) and the minimal surface roughness (Rq). The present work attempts to develop an appropriate machining strategy for a maximum process yield. MRR and Rq have been considered as measure of the machining performance with four different control parameters such as pulse on time, pulse off time, current and voltage. Experiments were conducted by Taguchi L18 mixed orthogonal array. A multiple linear regression model is developed to relate the input and output variables and GA is used to optimize WEDM process variables. The optimized results for maximum MRR and minimum Rq are compared with and without powder mixed dielectric and confirmation tests were conducted.