Microgrids (MGs) offer new technologies for semiautonomous grouping of alternative energy loads fed into a power grid in a coordinated manner. Simulations of these microgrids are time critical yet computationally demanding, inherently complex, and dynamic, especially when they are constructed for control purposes. In this paper, we address the design ranking and selection problem in MG simulations from a set of finite alternatives in the presence of stochastic constraints. Each design encapsulates a different level of control of the segregation mechanism within the system, and a performance function measured as a combination of the incurred cost and energy surety. Building on this performance function, optimal computing budget allocation (OCBA) method is used to efficiently allocate simulation replications for selecting the best design with significant accuracy and reasonable computational burden. Computational results on a multi-scale MG testbed have shown that OCBA algorithm outperforms equal and proportional to variance allocation of replications.