A technique for optimizing dynamic systems under uncertainty using a parallel programming implementation is developed in this article. A multiple-shooting discretization scheme is applied, whereby each shooting interval is solved using an error-controlled differential equation solver. In addition, the uncertain parameter space is discretized, resulting in a multiperiod optimization formulation. Each shooting interval and period (scenario) realization is completely independent, thus a major focus of this article is on demonstrating potential computational performance improvement when the embedded dynamic model solution of the multiperiod algorithm is implemented in parallel. We assess our parallel multiperiod and multiple-shooting-based dynamic optimization algorithm on two case studies involving integrated plant and control system design, where the objective is to simultaneously determine the size of the process equipment and the control system tuning parameters that minimize cost, subject to uncertainty in the disturbance inputs. (C) 2014 American Institute of Chemical Engineers