An Agent Based Model (ABM) is a powerful tool for its ability to represent heterogeneous agents which through their interactions can reveal emergent phenomena. For this to occur though, the set of agents in an ABM has to accurately model a real world population to reflect its heterogeneity. But when studying human behavior in less well developed settings, the availability of the real population data can be limited, making it impossible to create agents directly from the real population. In this paper, we propose a hybrid method to deal with this data scarcity: we first use the available real population data as the baseline to preserve the true heterogeneity, and fill in the missing characteristics based on survey and remote sensing datasets; then for the remaining undetermined agent characteristics, we use the Microbial Genetic Algorithm to search for a set of values that can optimize the replicative validity of the model to match data observed from real world. We apply our method to the creation of a synthetic population of household agents for the simulation of agricultural decision making processes in rural Zambia. The result shows that the synthetic population created from the farmer register can correctly reflect the marginal distributions and the randomness of survey data; and can minimize the difference between the distribution of simulated yield and that of the observed yield in Post Harvest Survey (PHS).