Ultraviolet (UV) disinfection is an environmental-friendly technology for water treatment. However, design and operation of UV disinfection reactors are very difficult without a good model. In this work, two modeling methods, Artificial Neural Network (ANN) and Genetic Programming (GP), were applied to model UV water disinfection reactors. The model training data were obtained from simulation using Computational Fluid Dynamics (CFD) software. The accuracy of these two modeling methods was compared based on modeling error as well as generalization ability for new inputs. The ANN and GP models were then used to determine optimal design and operating variables of UV disinfection process, using multi-objective optimization. Selected Pareto-optimal solutions were compared using CFD simulations, and the results are presented and discussed in this paper.