This article reviews how to design and analyze simulation experiments using two types of metamodels: low-order polynomial regression and Kriging. The type of metamodel used determines the design of the simulation experiment, which in turn determines the input combinations of the simulation model. For example, first-order polynomial regression should use a "resolution-III" design, while Kriging may use "Latin hypercube sampling". Before applying regression or Kriging, the inputs of a simulation model can be screened through "sequential bifurcation". Optimization of the system can be done using response surface methodology or Kriging models fitted through sequential designs like efficient global optimization. Lastly, robust optimization considers uncertainty in some simulation inputs.