Abstract: OBJECTIVE To optimize process parameters of docetaxel chitosan microspheres. METHODS The preparation was selected by L18(37) orthogonal design, and a mathematical model of relationship between the independent variables and dependent variables was established by using back-propagation(BP) artificial neural networks(ANN) and the process parameters were optimized with genetic algorithm(GA). RESULTS The optimum process parameters GA-predicted was established as follows: 3.730 8% as concentration percentage of chitosan, 0.500 4 g as amount of emulsifier, 1.843 3 as volume percentage ratio of organic phase to water phase, 25.027 7 as drug loading ratio, 2.246 5 mL as volume of glutaral, 63.419 1 min as duration of rotation and 611.922 8 r·min^-1 as rotation speed with the maximum drug loading 43.65 38%, the minimum span dispersity 0.594 0, and 8.168 5 μm as the mean diameter of docetaxel chitosan microspheres. Bias between observed and predicted values of the drug loading, the mean diameter and span dispersity of Docetaxel chitosan microspheres had no significant difference. CONCLUSION The multi-objective simultaneous optimization of process parameters in docetaxel chitosan microspheres preparation could be achieved by combining BP ANN modeling with GA. The models developed in this study were proved to be predictable and feasible.