Due to their high initial investment costs, and excessive energy consumption, autoclaves are costly pieces of machinery to operate in manufacturing composite materials. They play a critical role in the process since they feed the subsequent processes. For more effective autoclave operations, careful planning is necessary to decide what and when to load. Accordingly, the current research deals with a real-world case of autoclave loading and scheduling problems in a large-scale facility in an aerospace company. We introduce a single mixed-integer quadratically constrained programming to solve the facility's loading (bin-packing) and scheduling problems simultaneously. We utilize real-world data under different scenarios of changing the number of autoclaves and fixtures (tools that the prepared materials are laid on before entering the autoclave). Moreover, we present and test a two-stage approach to the same problem in that two main components are considered consecutively for faster solutions. Both single and two-stage approaches to the problem are elaborated along with their benefits and drawbacks. Nevertheless, the results of both methods reveal that the facility has a significant amount of saving potential. We also determine that a better course of action to improve the company's ability to meet demand would be to direct future investments towards fixtures rather than new autoclaves.