Understanding the complexity of urban expansion requires the analysis of the factors influencing the spatial rural-urban land conversion. This study aims to develop and evaluate a cellular automata (CA) spatial model to assist in understanding land use change patterns. Specifically, our CA simulation model is utilized to explore the effects of various factors on rural-urban land use conversion, including for example population density, slope, and proximity to roads. In addition, a genetic algorithm is developed to calibrate the CA model with an optimization focus placed on simulation accuracy. The CA model is validated using land use data from 2000 to 2004 in Nanjing, China. It is demonstrated that our approach provides an effective calibration support for modeling conversion of rural-urban land use.