Many processes in the food and bioproducts industry are of the batch type. The increased public awareness about product quality and environmental impact has stressed the need to improve this processes. This can be achieved using computer-aided optimal control methods. The selection of an appropriate algorithm is therefore critical. The advantages of alternative discretized decision methods over the traditional maximum principle have been indicated in the literature. Following these ideas, we have developed a computer package, ICRS/DS (Integrated Controlled Random Search for Dynamic Systems) for the optimization (optimal control) of batch processes, including distributed parameter systems. In a first step, the original optimal control problem is transformed in a constrained nonlinear optimization problem using an adequate parameterization of the control function(s). Finite differences/finite elements methods are used for the solution of PDE’s. In a second step, the constrained NLP problem is solved using a stochastic optimization algorithm. This procedure assures convergence with reasonable computation times. Two important operations were optimized using this algorithm : thermal processing of canned foods and air dehydration of bioproducts. Different objective functions (overall nutrient retention, quality factor retention, process time, energy efficiency, etc.), control functions and constraints were considered, resulting in several complex optimal control problems. In all cases, ICRS/DS proved to be a reliable and easy-to-use computational tool. In many cases, the calculated optimal control policies have significant advantages over the present operation conditions.