<p>Peaks in patients’ demand for inward hospitalization usually lead to disruptions in the provision of healthcare, negatively affecting patient and staff satisfaction. The two main sources of ward bed demand are the emergency department and the surgical center; while the former is random by nature, the latter can be managed through proper allocation of surgical specialties to time slots (or blocks) in the center’s timetable (Master Surgical Schedule—MSS) and efficient scheduling of surgical procedures within time slots across specialties. This case study explores the proposition and application of a three-step method to design a long-term MSS timetable at a tertiary public teaching hospital. In step 1, historical data are mined to determine the average duration of surgical procedures and the average length of stay in wards for each surgical specialty. Step 2 applies a genetic algorithm to generate a timetable that balances the overall ward bed demand across the planning horizon. In step 3, an alignment heuristic is employed to adjust the new timetable to resemble the hospital’s current MSS, minimizing disruptions to existing operations. The method was tested using data from the hospital, resulting in a significant reduction in post-operative ward bed demand variability by 99.9%, while retaining 97% of surgical specialties in their original slots. This case study presents the first approach for long-term MSS design that balances post-operative ward bed demand while minimizing changes to the current surgical teams’ schedules, emphasizing solutions that promote minimal disruption to hospital operations. Furthermore, the proposed method is adaptable for use in other hospitals with similar characteristics, offering a flexible solution to managing changes in surgical and ward bed demand.</p>