Density Functional Theory (DFT) and time-dependent Density Functional Theory (TD-DFT) are powerful tools for modeling orbital energy in conjugated molecules and have been useful tools for research in organic photovoltaics. In this work, DFT is first used to explain the red shift in the absorption spectrum and increased absorption observed in MEH-PPV. Initially, the modeling of the red-shift in the absorption peak of MEH-PPV is studied using Gaussian 03 software with the global hybrid functional B3LYP for exchange-correlation and the 6-31G basis set. DFT and TD-DFT are used to separately study the effects of polymer chain length, carbon-carbon double-bond stretching, and the polymer in solution vs. in gas space on red shift in absorption spectrum. Next, Gaussian 09 software and the same B3LYP functional and 6-31G basis set are used to study interchain and intrachain interactions of MEH-PPV in solution. The red shift in the absorption peaks for three MEH-PPV configurations (single-chain pentamer, two stacked pentamers, and decamer) are compared with experimental results for five different solvents (chloroform, toluene, xylene, dichloromethane, and chlorobenzene). This investigation indicates that inter-chain interactions dominate in “good” aromatic solvents as compared to “poor” non-aromatic solvents. The results suggest that inter-chain charge transfer interactions play a critical role in real solutions and inter-chain aggregation takes precedence over intra-chain aggregation in aromatic solvents. In the final section of the study, accurate values for the range-separation parameter (w) for three lengths of MEH-PPV polymer (trimer, tetramer, and pentamer) in five different solvents (chloroform, chlorobenzene, xylene, Tetrahydrofuran, and dichloromethane) are reported using the range-separated functionals wB97XD and CAM-B3LYP. Using these data, range separation parameters are predicted and used for longer polymer chains in chloroform solution. The differences in the range separation parameters for the different solvents is statistically significant and gives further insight into the polymer/solvent interaction.