Using computational modeling, we examine how varying the arrangement of binary mixtures of polymer grafted nanoparticles (PGNs) in a network affects the mechanical properties of the composite. The free ends of the grafted chains on the PGNs contain reactive groups that can form labile bonds with reactive ends on nearby PGNs, and thereby form extensive networks. This bond formation is reversible, with the bonds breaking and reforming at a specified rate. The two types of particles in the network differ in the strength of the labile bonds that they form with their neighbors, forming both relatively strong and weak interconnections. We examine the response of this dynamic network to tensile deformation when the binary PGNs are arranged in an alternating, layered structure or a random mixture. We determine the ultimate tensile properties (strength, toughness), the strain recovery and behavior under cyclic loading for samples with the layered and random architectures. We demonstrate that the layered structures display self-healing behavior and exhibit enhanced mechanical properties relative to the random system. Using our model, we can tune both the spatial and temporal characteristics of the hybrid material. Thus, the approach provides a useful tool for determining how to tailor these parameters to achieve superior mechanical behavior in PGN networks.