The Fixed-Charge Transportation Problem (FCTP) is an NP-hard combinatorial optimization problem that extends the classical transportation model by incorporating both fixed costs associated with the activation of routes and variable shipping costs. In this paper, a Genetic Algorithm (GA) enhanced with a priority-based decoding mechanism is proposed for efficiently solving the FCTP. The decoder maps a chromosome representation into a feasible transportation plan by utilizing a heuristic cost function combined with chromosome-derived priority values to guide shipment allocation. This approach effectively addresses the simultaneous optimization of mixed discrete decisions (route selection) and continuous variables (shipment quantities). The performance of the proposed algorithm is evaluated on a set of standard benchmark instances. Computational results demonstrate that the method is robust and effective, achieving high-quality solutions with improved convergence behavior. Moreover, the proposed priority-based decoding GA consistently yields competitive results and, in many cases, outperforms existing metaheuristic approaches, highlighting its potential as a powerful tool for solving complex logistics optimization problems.