To provide a dependency-aware application, multiple UAVs are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multi-objective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multi-objective optimization problem involving two objectives, i.e., the makespan minimization of all tasks and energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multi-objective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.