As demand for wind power continues to grow, it is becoming increasingly important to minimize the risk, characterized by the variance, that is associated with long-term power forecasts. This thesis investigated variance reduction in power forecasts from wind farm layout optimization.The problem was formulated as a multi-objective optimization one of maximizing mean-plant-power and minimizing variance. The ε−constraint method was used to solve the bi-objectiveproblem in a two-step optimization framework where two sequential optimizations are performed. The first is maximizing mean wind farm power alone and the second, minimizing variance with a constraint on the mean power which is the value from the first optimization. The results show that the variance in power estimates can be reduced by up to 30%, without sacrificing mean-plant-power for the different farm sizes and wind conditions studied. This reduction is attributed to the multi-modality of the design space which allows for unique solutions of high mean plant power at different power variances. Thus, wind farms can be designed to maximize power capture with greater confidence.