We investigated the production of biofungicide phenazine-1-carboxlic (PCA) by Pseudomonas sp. M18G, a gacA-deficient mutant of M18 for PCA high-production. Glucose was chosen as the optimal carbon source and soy peptone as the nitrogen source. A Plackett-Burman design revealed that glucose, soy peptone and NaCl were the most significant factors in PCA fermentation. Response surface methodology (RSM) and artificial neural network (ANN) models involving the significant factors were developed using common data. The prediction accuracy of ANN was slightly higher compared to RSM. The genetic algorithm (GA) was used to search the optimal input space of the trained ANN model and find the corresponding PCA yield. The optimum composition was found to be: glucose 34.3 gL(-1), soy peptone 43.2 gL(-1), NaCl 5.7 gL-1, and the predictive maximum PCA yield reached 980.1 mu g mL(-1). The optimized medium allowed PCA yield to be increased from 673.3 to 966.7 mu g mL(-1) after verification experiment tests. Additionally, PCA fermentation kinetics was investigated. Kinetic models based on the modified Logistic and Luedeking-Piret equations were developed, providing a good description of temporal variations of biomass (X), product (P), and substrate (S) in PCA fermentation.