Existing auxiliary learning approaches only consider the relationships between the target task and the auxiliary tasks, ignoring the fact that data samples within an auxiliary task could contribute differently to the target task, which results in inefficient auxiliary information usage and non-robustness to data noise. In this paper, we propose to learn a joint task and data schedule for auxiliary learning, which captures the importance of different data samples in each auxiliary task to the target task. However, learning such a joint schedule is challenging due to the large number of additional parameters required for the schedule. To tackle the challenge, we propose a joint task and data scheduling (JTDS) model for auxiliary learning. The JTDS model captures the joint task-data importance through a task-data scheduler, which creates a mapping from task, feature and label information to the schedule in a parameter-efficient way. Particularly, we formulate the scheduler and the task learning process as a bi-level optimization problem. In the lower optimization, the task learning model is updated with the scheduled gradient, while in the upper optimization, the task-data scheduler is updated with the implicit gradient. Experimental results show that our JTDS model significantly outperforms the state-of-the-art methods under supervised, semi-supervised and corrupted label settings.