Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and resource use. One possibility is using an intermediate “model of a model” (metamodel) built on experimental responses from the underlying simulation model and an optimization heuristic that leverages the metamodel to explore the input space more efficiently. However, determining the best metamodel and optimizer pairing for a specific problem is not directly obvious from the problem itself, and not all domains have experimental answers to this conundrum. This paper introduces a discrete multiple objective simulation metamodeling and optimization methodology that allows algorithmic testing and evaluation of four Metamodel-Optimizer (MO) pairs for different problems. For running our experiments, we have implemented a test environment in R and tested four different MO pairs on four different problem scenarios in the Operations Research domain. The results of our experiments suggest that patterns of relative performance between the four MO pairs tested differ in terms of computational time costs for the four problems studied. With additional integration of problems, metamodels and optimizers, the opportunity to identify ex ante the best MO pair to employ for a general problem can lead to a more profitable use of metamodel optimization.