Central oxidoreductase enzymes (eg, dehydrogenases, reductases) in microbial metabolism often have preferential binding specificity for one of the two major currency metabolites NAD(H) and NADP(H). These enzyme specificities result in a division of the metabolic functionality of the currency metabolites: enzymes reducing NAD+ to NADH drive oxidative phosphorylation, and enzymes reducing NADP+ to NADPH drive anabolic reactions. In this work, we introduce the computational method OptSwap, which predicts bioprocessing strain designs by identifying optimal modifications of the cofactor binding specificities of oxidoreductase enzyme and complementary reaction knockouts. Using the Escherichia coli genome-scale metabolic model iJO1366, OptSwap predicted eight growth-coupled production designs with significantly greater product yields or substrate-specific productivities than designs predicted with gene knockouts alone. These designs were identified for the production of L-alanine, succinate, acetate, and D-lactate under modeled conditions. Simulations predicted that production of L-alanine and D-lactate can be strongly coupled to growth by knocking out three reactions and swapping the cofactor specificity of one oxidoreductase reaction, while growth coupling was not predicted with four or fewer reaction knockouts under identical conditions. A succinate production design and an acetate production design were predicted to have higher maximum growth rates and higher substrate-specific productivities than designs predicted solely with reaction knockouts. The OptSwap formulation can be readily extended to additional organisms, and the constraints enforcing oxidoreductase specificity swaps can be extended to target other specificity sets of interest.