This paper presents a framework for optimal design of batch plants. It consists of a master optimization algorithm, i.e., a genetic algorithm (GA) coupled to a discrete-event simulation (DES). The innovative aspect of this work is the use of "shortcut" models included in the DES for describing the unit operations. The example of a protein production process serves as an illustration to show the effectiveness of the approach. The major interest is that the use of local models for unit operations allows the computation of an environmental index in combination with an economic indicator. The optimization framework determines the plant structure (parallel units, allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes) and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases at the liquid-liquid extraction,...). The results show that a plant configuration can be easily improved, only by changing the campaign policy for instance. Optimization results for monocriterion cases (miminization of investment cost and two environmental impact criteria based on biomass produced and amount of solvent used) illustrate the efficiency of the methodology, finding a set of "good" solutions which may be interesting for the decision maker.