Canopy stomatal conductance (g_c) controls transpiration and photosynthesis processes. Thus, the simulation of g_c and its environmental variation forms a significant component of many land surface models. A Jarvis-type model, which calculates g_c from a reference value multiplied by scaling (or response) functions of influencing environmental variables, is a typical representation of g_c in land surface modeling. Influential environmental factors often include solar radiation, vapor pressure deficit, and temperature and soil water conditions. Studies have applied different response functions to each individual environmental factor, often without rigorous evaluation. Thus, there is a need to determine which combination of response functions is most appropriate for a specific vegetation cover. In this study, an optimization model of g_c was determined for O. fragrans, an evergreen tree species in the southern China,based on field measurements. Sapflow, stem water potential, and microclimatic variables were recorded at an O. fragrans plantation site in 2013, where a severe drought occurred in July and August of that same year. Sap flow data were used to calculate transpiration, from which g_c was estimated from the inversed Penman-Monteith (PM) equation, based on micrometeorological data. Predawn stem water potential data were used to estimate root zone water potential, one of the environmental variables influencing g_c. Other environmental variables were available or could be derived from the micrometeorological measurements. A total of sixteen g_c models composed of different response functions were examined. Parameters of each candidate model were optimized using the DiffeRential Evolution Adaptive Metropolis(DREAM)model. DREAM runs multiple different chains simultaneously for global exploration and automatically tunes the scale and orientation of the distribution in randomized subspaces during the search for the optimized parameters. The measurement data points were separated to form two sets of data, one for parameter optimization using DREAM, and the other for model testing. The best model was determined based on the statistics of model testing results. The results indicate that this method is useful in determining the appropriate response function for each environmental factor in order to optimize the g_c model. For O. fragrans, an exponential function of vapor pressure deficit and root zone water potential, and a parabolic function of air temperature are the most appropriate response functions, whereas no significant difference is observed between different functions of solar radiation. The optimized model shows a significantly improved estimation of the g_c of O. fragrans, especially for the drought period. The correlation coefficient and root-mean-square error based on the model testing result were 0.803 and 0.000623 m/s, respectively. The results also suggest that the temperature stress function can be larger than one, a finding that is inconsistent with the conceptual definition of a stress function. Similar findings have been reported in previous studies. This discrepancy is likely attributed to the fact that air temperature and vapor pressure deficit are often strongly interdependent. Thus, to be conceptually consistent, the function of temperature and that of vapor pressure deficit should be combined into one single stress function. Further studies are required to examine if this result applies to other vegetation types globally.