This paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic Mixed Integer Linear Programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multi-period context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.