A mathematical model was developed to predict the optimum bin diameter, bin height, and number of bins based on user inputs of total storage volume, interest rate, unit cost of electricity, airflow rate, and annual number of hours of airflow. The grain storage system was divided into several cost components including land rent, site preparation, bins, fans, elevator, and energy. Algorithms were developed to predict the annual cost of each component as a function of size and number of bins. Scaling factors were included, allowing users to accommodate local conditions. These cost components were then combined to determine the total annual cost. An exhaustive search was employed to identify the bin diameter and number of bins which gave the lowest cost value. That combination was considered the optimum. Examples of how the optimum number and size of bins change with grain type, total volume of storage, and airflow rate are presented.