SEAPODYM-LMTL is a global advection-diffusion-reaction model that simulates age-structured zooplankton and micronekton populations driven by physical and biogeochemical forcing. This study introduces SeapoPym, a simplified version of this model that decouples biological dynamics from physical transport and incorporates a Genetic Algorithm (GA) for stochastic parameter estimation within the Python scientific ecosystem. Comparisons with SEAPODYM-LMTL show that omitting transport produces notable discrepancies in highly dynamic warm regions and cold environments with long zooplankton life cycles. However, SeapoPym remains suitable for simulating mesozooplankton across most warm- and temperate-ocean regions. Sobol sensitivity analysis identifies mortality parameters as key drivers of biomass magnitude and variability, with strong parameter interactions. Twin experiments highlight challenges in estimating recruitment-timing parameters and emphasize the importance of data from cold, contrasting environments. SeapoPym provides a flexible, low-cost framework for exploring parameter estimation, designing observational strategies, and addressing challenges in zooplankton model assessment, with the potential to integrate with circulation models or machine-learning emulators.