Class incremental learning has drawn much attention recently. Although many algorithms have been proposed for class incremental image classification, developing object detectors which can learn incrementally is still a challenge. Existing methods rely on knowledge distillation to achieve class incremental object detection (CIOD), which suffer from performance tradeoff between old and new classes. In this paper, we propose balanced ranking and sorting (BRS), to tackle the catastrophic forgetting and data imbalance problems for CIOD. Specifically, ranking \& sorting with pseudo ground truths (RSP) and ranking \& sorting transfer (RST) are developed to preserve the learned knowledge from the old model while learning new classes, in an unified framework. To mitigate the data imbalance problem, gradient rebalancing is performed with specific sample pairs. We demonstrate the effectiveness of our approach with extensive experiments on PASCAL VOC and COCO datasets, in which significant improvement over state-of-the-art methods is achieved.