Chemo-enzymatic pathway design aims to combine the strengths of enzymatic with chemical synthesis to traverse biomolecular design space more efficiently. While chemical reactions often struggle with regioselectivity and stereoselectivity, enzymatic conversions often encounter limitations of low enzyme activity or availability. Optimally integrating both approaches provides an opportunity to identify efficient pathways beyond the capabilities of either modality. Recently, studies have shown the advantage of leveraging enzymatic steps into industrial-scale chemical processes, such as for the blood sugar regulator Sitagliptin (Merck) and the HIV protease inhibitor Darunavir (Prozomix). Designing optimal chemo-enzymatic pathways is a complex task. It requires navigating a high-dimensional search space of potential reactions that combine individual chemical and biochemical steps while at the same time minimizing transitions between chemical catalysis and bioreactions. Here, we introduce an algorithmic approach, minChemBio, that relies on solving a mixed-integer linear programming (MILP) problem by optimally searching through known chemical and enzymatic steps extracted from the United States Patent Office (USPTO) and MetaNetX databases, respectively. minChemBio allows for the minimization of transitions between chemical and biological reactions in the pathway, thus reducing the need for costly separation and purification steps required. minChemBio was benchmarked on three case studies involving the synthesis of 2-5-furandicarboxylic acid, terephthalate, and 3-hydroxybutyrate. Identified designs included both established literature pathways as well as unexplored ones which were compared against pathways identified by existing retrosynthetic tools. minChemBio fills a current gap in the space of pathway retrosynthesis tools by controlling and minimizing the transitions between chemical catalysis and biocatalytic steps. It is accessible to users through open-source code (https://github.com/maranasgroup/chemo-enz).