Photosynthesis response to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. The genetic algorithm (GA), which is a robust stochastic evolutionary computational algorithm inspired by both natural selection and natural genetics, is proposed to simultaneously estimate the parameters [including maximum carboxylation rate allowed by ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (V(cmax)), potential light-saturated electron transport rate (J(max)), triose-phosphate utilization (TPU), leaf dark respiration in the light (R(d)) and mesophyll conductance (g(m))] of the photosynthesis models presented by Farquhar, von Caemmerer and Berry, and Ethier and Livingston. The results show that by properly constraining the parameter bounds the GA-based estimate methods can effectively and efficiently obtain globally (or, at least near globally) optimal solutions, which are as good as or better than those obtained by non-linear curve fitting methods used in previous studies. More complicated problems such as taking the g(m) variation response to CO(2) into account can be easily formulated and solved by using GA. The influence of the crossover probability (P(c)), mutation probability (P(m)), population size and generation on the performance of GA was also investigated.