Thermal energy storage can offer significant cost savings with time varying pricing. This study examines the effectiveness of using neural networks to model a district cooling system with ice storage for optimal control. Neural networks offer a fast performance estimation of a district cooling system with external inputs. A physics based model of the district cooling system is first developed to act as a virtual plant for the controller to communicate system states, in real time. Next, the neural network modeling the plant is developed and trained. This model is optimized using a genetic algorithm due to the on/off controls. Finally, a thermal load prediction algorithm is integrated to test under weather forecasts. It is shown through a case study that the optimal control scheme can effectively adapt to varying loads and varying prices to effectively reduce operating costs of the district cooling network by 16% for time of use pricing and 13% under real time pricing.