The new methodology presented provides a way to optimize the operation of a variety of batch processes (chemical, pharmaceutical, food processing, etc.) especially when at least one time-varying operating decision function needs to be selected. This methodology calculates the optimal operation without the use of an a priori model that describes in some accuracy the internal process characteristics. The approach generalizes the classical and widely used Design of Experiments (DoE) which is limited in its consideration of decision variables that are constant with time. The new approach, called the Design of Dynamic Experiments (DoDE), systematically designs experiments that explore a considerable number of dynamic signatures in the time variation of the unknown decision function(s). Constrained optimization of the interpolated response surface model, calculated from the performance of the experiments, leads to the selection of the optimal operating conditions. Two examples demonstrate the powerful utility of the method. The first examines a simple reversible reaction in a batch reactor where the time-dependant reactor temperature is the decision function. The second example examines the optimization of a penicillin fermentation process where the feeding profile of the substrate is the decision variable. In both cases, a finite number of experiments (4 or 16, respectively) lead to the very quick and efficient optimization of the process.