Efficient operating theatre scheduling is essential for optimising hospital resources and ensuring organized sessions of medical procedures. This process involves scheduling time blocks for each operating theatre over a planning period and allocating them to medical specialities, while considering constraints such as medical team availability and theatre compatibility. In this work, we model operating theatre scheduling as an optimization problem and apply Mixed Integer Linear Programming (MILP) techniques to improve theatre utilisation and enable medical teams to operate more patients. Our case study focuses on the University of Campinas’ Clinical Hospital, a regional high-complexity centre of excellence that serves a population of around 5 million people. Additional challenges include transplant surgeries, priority surgeries and scarcity of anaesthetists. To address these complexities, we develop a hierarchical multi-objective optimisation model that incorporates packing and knapsack-based constraints and can be solved efficient with an open-source MILP solver. This study includes a pilot implementation of the model within the hospital setting for validation and refinement. Preliminary results indicate that the model is able to not only replicate the schedules produced manually by a highly experienced staff member, but also improve the utilisation of the surgical centre with respect to the current practice.