Optimization is a pivotal point in distinguishing the competitiveness of industries that are developing, designing and operating various products and processes. Mineral processing is an industry which operates various sub-processes and produces one or several products. The sub-processes involved are dynamic in nature and differ in the discipline of operation. These dynamic sub-processes are sequentially integrated forming a mineral processing system. Currently, the developed simulations for the mineral processing systems have the potential to be used to design, operate and control mineral processing plants to an increased extent, but need broader optimization strategies to integrate multiple sub-processes involved. The scope of this research is to demonstrate application of multi-disciplinary optimization (MDO) architectures into a mineral processing simulation. A simulation study consisting of two sub-processes of comminution and classification circuits to produce aggregate products is used to demonstrate the application of MDO architectures. The MDO architectures are compared based on problem formulation, computational resources required and validity of the results. The optimization results using MDO architectures can be used to illustrate trade-offs between different sub-processes within the considered scope. The application of MDO architectures can facilitate the linking mathematical models of various disciplines such as comminution, and liberation in mineral processing simulation.