This paper introduces the Greedy Man Optimization Algorithm (GMOA), a novel bio-inspired metaheuristic approach for solving complex optimization problems. Inspired by competitive individuals resisting change, GMOA incorporates two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, and periodic parasite removal, which promotes diversity and avoids stagnation. The algorithm is evaluated on standard benchmark functions, including Sphere, Rastrigin, Rosenbrock, and Griewank, and its performance is compared with established algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO). Results demonstrate that GMOA outperforms these methods in terms of solution quality, convergence rate, and robustness. Statistical significance tests validate the reliability of the results. GMOA’s ability to balance exploration and exploitation makes it a promising tool for various real-world applications, including supply chain optimization and healthcare resource allocation.