The dynamic optimization (open-loop optimal control) of bioprocesses is considered. It is shown how these problems can be solved using a recently developed method, based on the control vector parametrization concept, which makes use of second-order sensitivities to obtain exact gradients and Hessians for the objective function of the underlying dynamic process model. A further extension of this scheme,which makes use of restricted second-order information, is also presented. This extension results in an efficient way to solve general dynamic optimization problems, even for high levels of control discretization. This new approach allows the efficient and robust solution of two challenging case studies regarding the optimal control of fed-batch bioreactors taken from the open literature.