The challenge of optimally sizing the variable renewable energy input and storage elements for different locations to ensure a cost-effective and constant ammonia output is addressed in this work. Utilising renewable energy sources to power ammonia production may lead to a substantial reduction of approximately 1% of global carbon emissions resulting from this activity. A largescale islanded green ammonia plant is first simulated in Python using appropriate models from the literature. A multi-objective genetic algorithm is then employed to find the optimal mix of solar, wind, battery, hydrogen and nitrogen storage, that will result in the lowest levelized cost of ammonia and a high capacity factor for the plant. Careful consideration is given to the initial population and selection strategies to ensure a diverse population, thereby preventing premature convergence and resulting in a globally optimal solution. The genetic algorithm generated multiple configurations with varying performance levels of which the best-performing configurations are showcased. The optimization is conducted at various locations across South Africa to determine an appropriate site for a green ammonia plant. The optimal configuration is subsequently analyzed by employing a sensitivity analysis to determine how the different aspects influence the levelized cost of ammonia and the capacity factor. The optimized plant’s performance is then compared with existing literature studies to compare the feasibility of an ammonia plant in South Africa.