We present a Genetic Algorithm (GA) tool that uses Sequential Approximate Optimization (SAO) to calibrate Agent-Based Models (ABMs). The SAO/GA searches through a user-defined set of input parameters to an ABM, delivering values for those parameters so that the output time series of an ABM match the real system's time series to certain precision. SAO/GA calculates a meta-model of the real and ABM's time series and optimizes that model. This allows SAO/GA to stabilize the ABMï¿1/2s time series and assure a higher probability of convergence, even under highly variable ABM's outputs. The results show that SAO/GA exhibits a higher convergence probability, but requires a rather long computational time to reach the stopping condition, although that long time is not so excessive to preclude SAO/GA practical use.