High Throughput Screening (HTS) systems are highly technological and fully automated plants which are used for the analysis of thousands of biochemical substances to provide basis for the drug discovery process. As the operation of these systems is remarkably expensive, the scheduling for the processes of such complex systems is critical to the HTS companies. Since the processing time affects the throughput and the efficiency of the system, a time-optimal schedule must be developed for the system which can yield high throughputs. In this thesis, a Mixed Integer Programming model is presented, minimizing the overall processing time and therefore maximizing the throughput of the system. To generate the mathematical model, the principles of Job-Shop Scheduling and Cyclic Scheduling are utilized. The results of the study are supported by an experiment conducted at the High Throughput Screening plant at Washington University in St. Louis. As a conclusion, the model has generated a time-optimal cyclic schedule which improves the total processing time of the system by 3 minutes for 25 batches. The projection of the model for experiments that run with hundreds of batches is interpreted to generate greater improvements for the overall processing time of the system.