Chemical processes can be described by nonlinear differential equations, typically with unknown model parameters. These models can be fitted to experimental data through parameter estimation. The estimated parameters are subject to statistical uncertainties, which depend on the type of experiments and can be calculated and minimized through optimal experimental design before their execution. In this contribution, these optimization problems are formulated, methods for their solution are discussed, and their effectiveness is demonstrated by applying them to a chemical reaction system.