Recent advances in multidimensional gas chromatography (MDGC) comprise methods such as multiple heart-cut (H/C) analysis and comprehensive two-dimensional gas chromatography (GC × GC); however, clear approaches to evaluate the MDGC results, choice of the most appropriate method, and optimized separation remain of concern. In order to track the capability of these analytical techniques and select an effective experimental approach, a fundamental approach was developed utilizing a time summation model incorporating temperature-dependent linear solvation energy relationship (LSER). The approach allows prediction of optimized analyte distribution in the 2D space for various MDGC approaches employing different experimental variables such as column lengths, temperature programs, and stationary phase combinations in order to evaluate separation performance (apparent 1D, 2D, total number of separated peaks, and orthogonality) for simulated MDGC results. The methodology applied LSER to generate results for nonpolar-polar and polar-nonpolar 2D column configurations for separation of 678 compounds in an oxidized kerosene-based jet fuel sample. Three-dimensional plots were generated in order to illustrate the dependency of separation performance on 2D column length and number of injections for different stationary phase combinations. With a given limit of analysis time, a MDGC approach to obtain an optimized total separated peak number for a particular column set was proposed depending on 1D and 2D analyte peak distribution. This study introduces fundamental concepts and establishes approaches to design effective GC × GC or multiple H/C systems for different column combinations, to provide the best overall separation outcomes with the highest separated peak number and/or orthogonality.