There is a need to optimize resources for large-scale environmental monitoring efforts, especially in developing countries. We tested a flexible framework to optimize the design (i.e., selection of study sites) of an environmental observatory network (EON) using publicly available data for Mexico. This country represents a challenge for designing EONs because of its megadiversity and large climate and ecological heterogeneity. We address three pervasive challenges for designing EONs: (1) How to characterize and delineate ecologically similar areas, (2) how to set geographic priorities to establish new representative study sites, and (3) how to assess the representativeness of current and potential new study sites. We used unsupervised cluster analysis to spatially delineate ecologically similar sampling domains. We identified the most representative sites within each domain using a conditioned Latin Hypercube-sampling strategy. Finally, we demonstrated the applicability of this approach by assessing the spatial representativeness of the eddy covariance network in Mexico (i.e., MexFlux). At least 84 distributed sampling sites are needed to represent >45% of the spatial heterogeneity of gross primary productivity (GPP) dynamics (i.e., GPP_mean and GPP_cv) and evapotranspiration (ET) dynamics (i.e., ET_mean and ET_cv) at the national level. The current array of MexFlux only represents 3% of GPP and 5% of ET dynamics spatial variability at the national-level, while the same number of sites organized under an optimal framework nearly doubled these estimates. Our framework is based on a data-driven approach and publicly available sources of information, so it could be applied anywhere in the world.