Dynamic optimization of industrial processes modeled by complex differential-algebraic equation systems is still a major challenge from an algorithmic point of view. We consider a special class of these problems, in which the objective is economic. The severe nonlinearity of this objective function relative to the smooth process behavior motivates the development of a new optimization algorithm called Successive Sequential Quadratic Programming (SSQP), which manages to drastically reduce the number of model integrations required to reach an optimum operating trajectory. The algorithm solved a 3800-variable (100 states) optimal grade transition problem for a HDPE reactor in less than 25 CPU minutes on a personal computer.