Prompt-based Continual Learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning. While arriving at a new session, existing prompt-based continual learning methods usually adapt features from pre-trained models to new data by introducing prompts. However, these prompts lack an optimization objective explicitly modeling inter-session class relationships, thus failing to construct clear inter-session class margins. Moreover, some old samples use new prompts during inference, resulting in the prompt-ambiguity overlap space - a special situation where old and new class spaces overlap. To address these issues, we propose an innovative approach called RCS-Prompt to rearrange class space by bidirectionally optimizing prompts. RCS-Prompt optimizes prompts to signify discriminative regions across different sessions in the class space. Additionally, it mitigates the prompt-ambiguity overlap space by altering the labels of a small subset of new samples to old classes and training them with a customized symmetric loss. The proposed method effectively reduces the overlap between old and new class spaces, thereby establishing clear inter-session class margins. We extensively evaluate RCS-Prompt on public datasets, demonstrating its effectiveness in prompt-based continual learning.