In silico screening of CO2 adsorbents is a very powerful method for preselecting the most promising porous solids for experimental studies. Because of increased computational power, it is now possible to investigate a large number of adsorbents in a fairly short time. However, it remains difficult to rationalize structure-performance correlations because the complexity of such porous materials cannot be reduced to a few simple descriptors. In this paper, we present a different approach that was applied to the design of an optimal adsorbent for CO2 separation from gas mixtures. A CO2-CH4 mixture was used as the feed. We constructed a simplified model of the porous material by adopting a spherical pore geometry. From the dispersion repulsion point of view, the spherical pore was modeled by homogeneously distributed Lennard-Jones sites. Besides, a charge distribution was introduced to mimic the electrostatic behavior. The selected charge distribution is constituted of negative charges homogeneously distributed over the surface of the pore. The total negative charge is counterbalanced by eight positive discrete charges placed on the corners of the cube inscribed in the sphere. This model can be characterized by two main descriptors: the pore size and the cation charge. For this model, the adsorbate adsorbent interaction potential due to electrostatic and dispersion repulsion interactions was determined. Then, the Henry constants of CO2 and CH4 are calculated from statistical thermodynamics and substituted in the Ruthven statistical model, which allows calculation of the binary adsorption isotherms. Finally, the adsorbed quantities are used to estimate the performance indexes of the pressure swing adsorption (PSA) process, such as the working capacity and separation factor. We show how these two parameters depend on the pore size and cation charge. For a medium-pressure PSA process, the optimal adsorbent has a large pore volume and is highly charged, which provides high working capacity and selectivity. The high cationic charge allows one to efficiently fill the large pore volume and assures high CO2 selectivity.