The addition of polyethylene glycol (PEG) chains to poly(lactic-co-glycolic acid) (PLGA) matrices is extensively used to modulate the biodegradation, drug loading and release, mechanical properties, and chemical stability of the original system. Multiple parameters, including the molecular weight, relative concentration, polarity, and solubility, affect the physicochemical properties of the polymer blend. Here, molecular dynamics simulations with the united-atom 2016H66 force field are used to model the behavior of PLGA and PEG chains and thus predict the overall physicochemical features of the resulting blend. First, the model accuracy is validated against fundamental properties of pure PLGA and PEG samples. In agreement with previous experimental and theoretical observations, the PLGA solubility results to be higher in acetonitrile than in water, with Flory parameters nu(ACN) = 0.63 +/- 0.01 and nu(W) = 0.21 +/- 0.02, and the Young's modulus of PLGA and PEG equal to Y = 2.0 +/- 0.43 and 0.32 +/- 0.34 GPa, respectively. Next, four PEG/PLGA blending regimes are identified by varying the relative concentrations and molecular weights of the individual polymers. The computational results demonstrate that at low PEG concentrations (<8% w/w), homogeneous blends are generated for both low and high PEG molecular weights. In contrast, at comparable PEG and PLGA concentrations (similar to 50% w/w), short PEG chains are only partially miscible whereas long PEG chains segregate within the PLGA matrix. This behavior has been confirmed experimentally via differential scanning calorimetry and is in agreement with previous observations. Finally, the computed Young's modulus of PLGA/PEG blends is observed to decrease with the PEG content returning the lowest values for the partial and fully segregated regimens (Y approximate to 1.3 GPa). This work proposes a computational scheme for predicting the physicochemical properties of PLGA/PEG blends paving the way toward the rational design of polymer mixtures for biomedical applications.