Occupant-centric control (OCC) strategies represent a novel approach to indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings before implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that can optimize such performance is an important step that faces logistical constraints during field implementations. This research presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB models with OCCs that learn their preferences. The framework features a parallel processing structure to obviate the computational burden and enhance optimization efficiency. A sensitivity analysis is conducted to identify the most influential variables on OCC performance in terms of increasing energy efficiency and occupant comfort. A two-step multi-objective optimization is then developed to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort. Results revealed significant improvement in OCC performance when they were customized with the identified optimal settings for different occupants. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation.