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Genetic Algorithms have been successfully used for a long time in solving optimization problems in so many diversified fields. Most of the research effort focused on devising a suitable mapping for the problem in hand, or proposing efficient types of operations, finding an optimal set of parameters like crossover and mutation rates, mutation step size and crossover format, or selection methods towards optimizing the search in the sense of reducing the run time, computational burden, escaping local minima, etc.. In this research work, we present an intensive mutation with fitness based step size as a main player in the space exploration and exploitation. The test results show that mutation can be as good as the crossover operation in upgrading the population fitness, and the longer time it takes to execute population-wide mutation pays off in terms solution quality under time constraint and chance of getting an optimal solution, compared to the classical genetic algorithm.