Vegetation models are indispensable tools for investigating and projecting the terrestrial carbon cycle, both as standalone models and embedded in global climate models. However, current models vary widely in their representation of ecosystem processes and consequently in their projected future carbon dynamics. Eco-evolutionary optimality (EEO) approaches, which derive and test hypotheses about optimal plant behaviour under specific environmental conditions as a consequence of natural selection, have been proposed as a means to improve the reliability of vegetation models and the robustness of their future projections. Here we embed EEO-derived models for photosynthesis and leaf dark respiration, and their acclimation to changing conditions, into the widely used LPJ-GUESS vegetation model. We evaluated the simulated gross primary production (GPP) patterns against remotely-sensed GPP derived from sun-induced fluorescence and found that the EEO configurations improved the spatial distributions (a mean reduction in error of 15 % across gridcells) and global interannual variability (a mean reduction in error of 32 % after accounting for differences in global totals) compared to the standard version of LPJ-GUESS. Evaluation against GPP fluxes from eddy flux covariance measurements also showed improved performance, the <em>R<sup>2</sup></em> of 5-day GPP increased from 0.45 to 0.48 (averaged across 147 sites). The simulated global carbon pools, fluxes, burnt area and biome distributions were not impacted substantially. The improvements were achieved with no alteration to processes except photosynthesis, respiration and plant water uptake, and with no recalibration or tuning. The EEO configuration also reduced model run time and eliminated the need for poorly-constrained PFT-dependent parameters governing the temperature response of photosynthesis. As well as being a tangible improvement to LPJ-GUESS, this study further confirms the usefulness of EEO approaches to improve global vegetation models.