To meet service level agreement (SLA) requirements, the majority of enterprise IT infrastructure is typically overprovisioned, underutilized, non-compliant and lacking in required agility resulting in significant inefficiencies. As enterprises introduce and migrate to next-generation applications designed to be horizontally scalable, they require infrastructure that can manage the duality of legacy and next generation application requirements. To address this, composable data center infrastructure disaggregates and refactors compute, storage, network and other infrastructure resources in to shared resources pools that can be “composed” and allocated on-demand. In this paper, we model a theorical problem of resource allocation in a composable data center infrastructure as a bounded multidimensional knapsack and then apply multi-objective optimization algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Generalized Differential Evolution (GDE3), to allocate resources efficiently. The main goal is to maximize resource availability for the application owner, while meeting minimum requirements (in terms of CPU, memory, network, and storage) within budget constraints. We consider two different scenarios to analyze heterogeneity and variability aspects when allocating resources on composable data center infrastructure.