Microbial production of chemicals through metabolic engineering has garnered significant interest in recent years due to growing concerns over the sustainability of conventional chemical production processes. Biochemical production offers an environmentally benign alternative through the use of renewable substrates and the ability to catalyze reactions at mild reactor conditions. While advances in bioengineering and systems biology have enhanced our understanding of microbial metabolism and led to the development of tools and techniques for strain design, several rounds of the design-build-test-learn cycle of strain engineering are necessary before a strain with commercial viability can be realized. Bioprocesses with decoupled growth and chemical production stages – where metabolic states are dynamically controlled, have been proposed as a solution to low chemical productivity. In this work, we explore the design principles that govern the effective implementation of such dynamic control strategies and two-stage chemical production processes to develop bioprocesses that offer optimal Titer, Rate, and Yield (TRY) values. To this end, we first build a computational framework that predicts novel two-stage chemical production processes that use a growth-coupled chemical production stage to outperform one-stage alternatives over a wide range of substrate uptake concentrations. Further, this algorithm also predicts optimal operating points that result in enhanced chemical production characteristics and reveals some common features of ideal production stage flux distributions. Next, in order to facilitate the characterization of large strain and genetic circuit libraries required to validate our experimental hypotheses, we develop an eco-friendly automation platform that effectively characterizes cellular phenotypes. Then, we used a model-based design strategy to improve the switching characteristics of a genetic toggle switch – a gene regulatory device that allows the experimental implementation of dynamic control strategies. Specifically, we were able to improve switching speeds and show a trade-off between the speed and the robustness of the switch. Finally, we validated our model findings by building a large library of toggle switch variants that offer a wide range of switching speeds. We anticipate that these design principles along with the switch library will facilitate the widespread implementation of two-stage chemical production processes.