This work addresses the problem of determining the optimal operating schedule that minimizes the operating cost in an energy-intensive air separation plant. The difficulty arises from the fact that the rate at which the utility company supplies electricity to the plant is subject to high fluctuations. This creates a potential opportunity to reduce average operating costs by changing the operating mode and production rates depending on the power costs, However, constraints occur due to product distribution requirements and plant capabilities, The scheduling optimization problem is made more challenging because the power prices are only known for a portion of the desired optimization horizon. These challenges were addressed by developing an efficient two-stage stochastic programming approach. Extensive analysis was done which resulted in a MILP problem formulation that uses an ARIMA model to generate the necessary scenarios for future power prices. The proposed problem was solved by utilizing commercial software and has been successfully tested on real data.