This paper presents a mission-centric strategy for control of the engine and electrical microgrid of a More Electric Aircraft (MEA). A Markov chain is used to represent the MEA mission profile with each state covering a stage in the mission and the probabilistic transitions between states representing the mission progression. Reference scenario trees are generated from potential pathways through the Markov chain and passed to a Stochastic Model Predictive Controller (S-MPC). The S-MPC uses a scenario based formulation to optimize over a probability weighted average of the cost function with constraints enforced over all scenarios. Tight couplings in the dynamics of the subsystems require their careful coordination for safe and acceptable performance from the MEA. Simulations on a nonlinear model of the MEA demonstrate the ability of the S-MPC to closely match the performance of an MPC with perfect preview of the future reference profiles and outperforms an MPC given no preview.