Unsupervised domain adaptive object detection (DAOD) aims to adapt detectors from a labeled source domain to an unlabelled target domain. Existing DAOD works learn feature representations with both class discriminative and domain invariant by jointly minimizing the loss across domain alignment and detection tasks. However, joint resolution of different tasks may lead to conflicts, with one contributing factor being gradient conflicts during optimization. If left untouched, such disagreement may degrade adaptation performance. In this work, we propose an efficient optimization strategy named Conflict-Alleviated Gradient descent (CAGrad) which aims to alleviate the conflict between two tasks (i.e., alignment and classification). Particularly, we alter the gradients by projecting each onto the normal plane of the other. The projection operation changes conflicting gradients from obtuse angles to acute angles, thus alleviating the conflict and achieving gradient harmonization. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including cross-camera, weather, scene, and synthetic to real-world adaptation. Extensive experiments on multiple DAOD benchmarks demonstrate the effectiveness and superiority of our CAGrad.