Ground-based and satellite atmospheric observations are essential for reducing uncertainties in methane (CH₄) emissions by atmospheric inversion, particularly in data-sparse regions such as Africa. However, adding new observation sites does not yield linear improvements of emission uncertainties because overlapping transport sensitivities reduces marginal information gain. Here we develop a Bayesian framework to strategically optimize CH₄ observation network design for column retrievals from upward-looking Fourier Transform Infrared (FTIR) spectrometers (e.g., EM27/SUN), jointly identifying the optimal number of sites and their spatial configuration. The framework quantifies uncertainty reduction for grid-point (1°) total and sectoral emissions while accounting for transport redundancy, cloud screening, and observational errors. Using January and July as representative months, we find that uncertainty reduction increases rapidly during early network expansion but gradually saturates beyond a certain number of additional sites. An optimized configuration of ten new sites added to the existing network achieves over 65 % reduction in prior uncertainty for total African CH₄ emissions in both months, with comparable improvements across fire, wetland, and anthropogenic sectors. Sensitivity analyses indicate that while the optimal number of sites varies with assumptions about cloud filtering, the spatial configuration remains robust, supporting cost-effective observation network design in data-sparse regions.