Crushing is a harsh process and production units are subjected to wear and failure over time which will reduce the overall performance of the plant. To achieve optimum process performance, both time dependant process dynamics and operating conditions should be taken into account. In this paper the aim is to create a framework for simulating the process from a more operational perspective to evaluate process performance and process optimum for different operational scenarios. The objective is to model and simulate the discrete phenomena that can cause the process to alter performance and implement it with dynamic process simulations. A method for combining discrete probability simulations with time-continuous simulations for process evaluation and optimization is presented. The proposed framework demonstrates a systematic approach to evaluate the process performance and locating optimum process configuration, for a given condition. The developed models can be used to optimize different aspects of the operation depending on the defined objective function and the system boundaries. Optimization of process throughput by manipulating configuration of both the grizzly and the crushers, as well as the time between calibrations has been illustrated in this paper. Adjusting the process continuously and calibrating it at the appropriate time can have major benefits when it comes to the process availability and utilization, increasing performance by 4.1-9.3 % in these cases. Evaluation of process robustness with regards to different maintenance strategies and process variation gave an indication of the process and unit performance under a long operating period. By combining discrete and dynamic simulation, a higher simulation fidelity can be achieved to provide a more operational perspective to the optimization and process analysis.