In this paper, we consider the problem of synthesizing online optimal flight controllers, in the presence of multiple objectives. The problem is cast as an adaptive Multi-Objective Optimization (MO-Op) flight control problem, in which a control policy is sought that attempt to optimize over multiple, sometimes conflicting objectives. A solution strategy utilizing Gaussian Process (GP) based adaptive-optimal control is presented, in which the system uncertainties are learned with an online updated budgeted GP. The mean of the GP is used to feedback linearize the system and reference model shaping is utilized for optimization. To make the MO-Op problem online realizable, a relaxation strategy that poses some objectives as adaptively updated soft constraints is proposed. The strategy is validated on a nonlinear roll dynamics model with simulated statedependent flexible-rigid mode interaction.