One of the greatest challenges in highly regulated industries, such as pharmaceuticals and agrochemicals, is the process of selecting, developing, and efficiently manufacturing new products that emerge from the discovery phase. This process involves the performance of regulatory tests, such as environmental and safety tests, for the new products and the plant design for manufacturing the products that pass all tests. To systematically address this problem, we consider the simultaneous optimization of resource-constrained scheduling of testing tasks in new product development and design/planning of batch manufacturing facilities. A multiperiod mixed-integer linear programming (MILP) model that maximizes the expected net present value of multiple projects is proposed. The model takes into account multiple tradeoffs and predicts which products should be tested, the detailed test schedules that satisfy resource constraints, design decisions for the process network, and production profiles for the different scenarios defined by the various testing outcomes. To solve larger instances of this problem with reasonable computational effort, a heuristic algorithm based on Lagrangean decomposition is proposed. The algorithm exploits the special structure of the problem, and computational experience shows that it provides optimal or near-optimal solutions, while being significantly faster than the full-space method. The application of the model is illustrated with three example problems.