The vessels are powered by propellers that convert engine power into movement. Optimizing propeller design involves numerous variables like diameter and blade count, making exact methods impractical. Meta-heuristics, such as evolutionary algorithms, offer a promising solution. This study introduces a novel marine propeller optimization approach. It decomposes the optimization process and proposes a new fitness function to overcome previous limitations. Based on two continuous optimization algorithms, the approach was compared to the state-of-the-art differential evolution algorithm. Results from ferry-boat propeller design experiments show the proposed approach achieves approximately 1% higher efficiency than the baseline study at 7.0 and 7.5-knot speeds. The proposed approach succeeds at 8.0 and 8.5 knots, where the baseline failed. Additionally, decomposition reduces execution time by executing in six threads.