The efficient scheduling of surgeries in hospital operating rooms is a critical task that directly impacts healthcare service quality and resource utilization. This paper addresses the Surgical Scheduling Problem, a combinatorial optimization challenge characterized by multiple constraints and high computational complexity. Two nature-inspired metaheuristics are compared: a genetic algorithm and a discrete variant of particle swarm optimization. The methods are evaluated on synthetic instances designed to reflect realistic hospital scenarios, considering solution quality, computational efficiency, and compliance with institutional constraints. Over 300 Monte Carlo runs, both approaches consistently produced feasible schedules within practical time limits. Statistical testing revealed significant differences in central tendency (Mann–Whitney \(p < 0.001\) ; effect size \(d = -0.424)\) . The genetic algorithm achieved lower typical makespans and more stable distributions, whereas the particle swarm approach identified the single best schedule (1095.28 min). The typical performance gap between both methods was small, usually below five minutes, highlighting their complementary strengths: the genetic algorithm provides robustness for routine planning, while the particle swarm strategy offers exploratory capacity for instance-specific optimization. These results support the use of metaheuristic strategies to enhance operating-room scheduling in public healthcare contexts.