Through the application of evolutionary mechanisms such as selection, crossover, and mutation, GAs are posited to achieve near-optimal solutions with greater rapidity and reduced computational burden compared to conventional MILP methods, particularly in instances where exact optimization proves to be computationally prohibitive. GAs have been widely used in sensor placement optimization across various applications, including structural health monitoring and seismic building monitoring. While GAs do not guarantee finding the exact optimal solution, their high efficiency in finding a sub-optimal solution that is very close to the optimum makes them well-suited for applications in methane emissions detection on oil and gas sites, where errors in the modeled emissions make an exact solution superfluous.