Antarctica's unique research environment necessitates innovative project selection and scheduling strategies to maximize scientific output while addressing logistical, environmental, and resource constraints. This study introduces a novel framework for the Research Project Selection and Scheduling in Multiple Antarctic Stations Problem (RPSAP), formulated as a mixed-integer programming model. The model incorporates resource-sharing constraints, station-specific capacities, transportation delays, and sustainability considerations. Given the problem's NP-hard complexity, three metaheuristic methods-Iterated Local Search (ILS), Variable Neighborhood Search (VNS), and Simulated Annealing (SA)-were developed to efficiently solve large-scale instances. Metaheuristics demonstrated robust performance through extensive computational experiments involving 480 test instances across 60 classes. The ILS consistently outperformed others in solution quality and scalability, while SA offered competitive results in execution time. Results reveal that the metaheuristics are superior to exact methods in handling large problem sizes, with optimality achieved only for small instances using the proposed exact model. This research bridges the gap between theoretical optimization models and practical applications, offering decision-making tools for research managers to enhance resource utilization, minimize ecological impacts, and prioritize high-impact projects.