In this paper, a discrete-event simulation model is coupled with a genetic algorithm to treat highly combinatorial scheduling problems encountered in a production campaign of a fine chemistry plant. The main constraints and features of fine chemistry have been taken into account in the development of the model, thus allowing a realistic evaluation of the objective function used in the stochastic optimization procedure. After a presentation of problem combinatorics, the coupling strategy is then proposed and illustrated by an example of industrial size (24 equipment items, 140 products, 12 different production recipes and 40 products to be recycled during the campaign). This example serves as an incentive to show how the approach can improve production performance. Three technical criteria have been studied: campaign completion time, average product cycle time, respect of due-dates. Two kinds of optimization variables have been considered: product input order and/or allocation of heuristics for conflit treatment. The results obtained are then analysed and some perspectives of this work are presented.