The liquid-liquid extraction process is well-known for its complexity and often entails intensive modeling and computational efforts to simulate its dynamic behavior. This paper presents a new application of the Genetic Algorithm (GA) to predict the modeling parameters of a chemical pilot plant involving a rotating disc liquid-liquid extraction contactor (RDC). In this process, the droplet behavior of the dispersed phase has a strong influence on the mass transfer performance of the column. The mass transfer mechanism inside the drops of the dispersed phase was modeled by the Handlos-Baron circulating drop model with consideration of the effect of forward mixing. Using the Genetic Algorithm method and the Numerical Analysis Group (NAG) software, the mass transfer and axial dispersion coefficients in the continuous phase in these columns were optimized. In order to obtain the RDC column parameters, a least-square function of differences between the simulated and experimental concentration profiles (SSD) and 95 % confidence limit in the plug flow number of the transfer unit prediction were considered. The minus 95 % confidence limit and sum of square deviations for the GA method justified it as a successful method for optimization of the mass transfer and axial dispersion coefficients of liquid-liquid extraction columns.