<p>This study presents an innovative application of genetic algorithm (GA) to optimize the classical fuzzy transportation problem (FTP). We keep the availability and demand variables in crisp form while maintaining the cost parameters of the classical transportation model in fuzzy form. By transforming the fuzzy problem into a pointwise crisp problem, we then apply existing methods to find possible solutions. These solutions, particularly the unit allocations, serve as the initial population for the GA. The GA then proceeds to yield an optimized solution, achieved through the strategic selection of parents from the initial population and the implementation of crossover and mutation operations. This research reveals a significant reduction in costs when GA is employed in conjunction with solutions derived from traditional methods. To elucidate this approach, the study details its application in two distinct numerical examples where the costs are denoted as triangular and trapezoidal fuzzy numbers. These examples effectively demonstrate the enhanced optimization capabilities of genetic algorithm in the context of fuzzy transportation problems, indicating a promising avenue for future research and practical applications.</p>