CO2 solubility in oil is a key parameter in CO2 flooding process. It results in oil swelling, increased oil density, and decreased oil viscosity. Laboratory studies needed to cover a wider range of data, and are time consuming, costly, and may be not available or possible in many situations. On the other hand, although various models and correlations are useful in certain situations, they may are not be applicable in many situations. In this study, a new genetic algorithm-(GA)-based technique has been used to develop more reliable correlations to predict CO2 solubility, oil swelling factor (SF), CO2-oil density, and viscosity Of CO2-oil mixtures. Based on the Darwinian theory, the GA technique mimics some of the natural process mechanisms. Furthermore, GA-based model correlations recognize all the major parameters that affect each physical property and also well address the effects Of CO2 liquefaction pressure. Genetic algorithm-based correlations have been successfully validated with published experimental data. In addition, a comparison of these correlations has been made against widely used correlations in the literature. It has been noted that the GA-based correlations yield more accurate predictions with lower errors than all other correlations tested. Furthermore, unlike other correlations that are applicable to limited data ranges and conditions, GA-based correlations have been validated over a wider range of data.