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For the code fault identification technique, mutation testing is the most resourceful and cost-effective testing approach. The various tiers of procedures required to create the test case scenario increase the cost. However, in order to work effectively, an application must pass such test scenarios. Several ways have been proposed to reduce the cost of mutation testing. The goal was to use genetic algorithms and multi-objective particle swarm optimization to reduce the cost of producing test cases. However, the smallest value cannot be obtained since it is reliant on the problem's borders and the search space region. All optimization techniques in this case have the same goal function. The variables reachability, necessity, and control determine the cost function. The optimization strategy, which is related to mutation testing, is used to identify the minimal value of the cost function by rigorous searching. The proposed multi-objective surrogate-based optimization process addresses previous techniques' weaknesses. Because the primary goal of surrogate optimization is to find the smallest possible value for the objective function. When compared to prior methods, this feature aids in the efficient reduction of expenditures. The variables availability, demand, and management determine the cost function. The optimal value for this cost function will be discovered using our suggested method, yielding the best test case at the lowest cost. The proposed approach was developed and validatedwith the help ofemujava, and it achieves an increased mutation score in less rounds. It can also detect suspicious mutations, which speeds up the procedure and reduces the number of test cases.