Malonyl-CoA is the major precursor for the biosynthesis of diverse industrially valuable products such as fatty acids/alcohols, flavonoids, and polyketides. However, its intracellular availability is limited in most microbial hosts, hampering the industrial production of such chemicals. To address this limitation, we present a multilevel optimization workflow using modern metabolic engineering technologies to systematically increase the malonyl-CoA levels in Pseudomonas putida. The workflow involves the identification of gene downregulations, chassis selection, and optimization of the acetyl-CoA carboxylase complex through ribosome binding site engineering. Computational tools and high-throughput screening with a malonyl-CoA biosensor enabled the rapid evaluation of numerous genetic targets. Combining the most beneficial targets led to a 5.8-fold enhancement in the production titer of the valuable polyketide phloroglucinol. This study demonstrates the effective integration of computational and genetic technologies for engineering P. putida, opening new avenues for the development of industrially relevant strains and the investigation of fundamental biological questions.