Water alternating gas (WAG) is an enhanced oil recovery (EOR) method integrating the improved macroscopic sweep of water flooding with the increased microscopic displacement of gas injection. The optimal design of the WAG operating parameters is usually based on numerical reservoir simulation via trial and error. In this study, robust evolutionary algorithms are utilized to automatically optimize hydrocarbon WAG performance in the E-segment of the Norne field. Net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimization (PSO), are used to optimize over an increasing number of operating parameters. The operating parameters include water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas and the total WAG period. In progressive case studies, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. We also optimize the incremental recovery factor (IRF) within a fixed total WAG simulation time. The distinctions between the WAG parameters found by optimizing NPV and oil recovery are highlighted. This is the first known work to optimize over such a wide set of WAG variables and the first use of PSO to optimize a WAG project at the field scale. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimized over all the above WAG operating variables, and 14.2% and 16.2% higher, respectively, if the IRF is optimized.