Microbial growth is constrained by physicochemical and spatial limitations that shape how cells utilize available nutrients. Over evolutionary timescales, microorgansims have optimized the allocation of protein resources to thrive across diverse environments, with ranging nutrient availabilities. This raises the question of whether extracellular metabolite exchange rates, i.e. all molecules consumed or produced by the cell, can serve as quantitative fingerprints of the intracellular catalytic efficiencies that evolution has shaped. To explore the relation between the environment and enzyme efficiencies, we combined sEnz, a method to quantify the flux control of enzymes, with a genetic algorithm that evolves kcat values in Protein Allocation Models (PAMs) toward experimentally observed fluxes. The PAMparametrizer framework reproduced key physiological traits and accurately reflected environmental influences on intracellular metabolism, generating an ensemble of improved models. Applied to Escherichia coli and Corynebacterium glutamicum, the resulting PAMs better captured metabolic behavior than the initial models. For the metabolically versatile Pseudomonas putida, limited experimental measurements allowed only recovery of the extracellular phenotype , emphasizing the value of complete physiological datasets for complex metabolic systems. The PAMparametrizer, by linking exchange fluxes to enzyme kinetics, not only deepens our understanding of metabolic refinement over evolutionary timescales but also establishes a foundation for scalable, evolution-informed model parametrization across organisms and conditions.