This paper describes the application of a genetic algorithm to the development of a solvent-additive SAGD process. A review of related field projects and key simulation studies is provided, together with a discussion of the pros and cons of potential alkane solvents. Economics and the impact of dynamic and ultimate retention are discussed. A general conclusion drawn from literature is that optimal solvent application to SAGD will likely involve time variations in both rate and composition of the solvent. This results in an optimization problem that has a large number of dimensions, and is nonlinear. We have found genetic algorithms, which mimic biological evolution, have been found to be extremely effective in addressing such problems. The general methodology of application to solvent additives by Laricina Energy Ltd. is described. A key product of this effort, optimized for a simple elastic reservoir, is presented. The genetic algorithm produced an operable process, which could be described as a new combination of preexisting concepts. The process offers material improvements in thermal bitumen supply costs, as well as recovery factor. Major reductions in the physical steam/oil ratio (SOR), (and therefore) capital intensity and carbon emissions, are indicated.