Abstract: Microcystis aeruginosa bloom is a major harmful Cyanobacteria bloom in lakes and reservoirs. Researches on methods for prediction and forecasting of algal blooms are of important significance to mitigating harm of the blooms. Based on the CE-QUAL-W2 model, indoor growth of Microcystis aeruginosa, a dominant species, typical of Cyanobacteria blooms was simulated with the aid of the PIKAIA genetic algorithm for analysis of sensitivity of different growth kinetci parameters to its growth, and threshold of sensitive parameters. Results show that the CE-QUAL-W2 model is good at simulating indoor growth process of Microcystis aeruginosa, and AG (maximum algal growth rate), AS (algal settling rate), ASAT (light saturation intensity at maximum photosynthetic rate), ALGN (ALGP)stoichiometric equivalent between algal biomass and nitrogen (phosphorus), and ACHLA (ratio between algal biomass and chlorophyll a) are the sensitive parameters that affect growth of Microcystis aeruginosa. Among them, AG and ACHLA are positively related to Chlorophyll a peak, while AS, ASAT, ALGP and ALGN are negatively related. Besides, AS and ACHLA are positively related to the time Chlorophyll a peak appears, while AG, ALGP and ALGN are negatively related. Variation of ACHLA can only multiply or reduce Chlorophyll a by time, but it will not affect the time the peak appears. Optimal values of the six parameters are defined as follows:AG=7.526 8 d^-1, AS=0.002 2 m·d^-1, ASAT=102.774 4 W·m^-2, ALGP=0.000 5, ALGN=0.041 3, ACHLA=0.125 8 mg·μg^-1.