The fundamental challenge in Multivariate Time Series forecasting is effectively modeling complex temporal dependencies and variable correlation. Transformer-based models achieve breakthroughs but face challenges with quadratic complexity and permutation invariant bias. A recent model, Mamba, has emerged as a competitive alternative. However, we observe that the issue of scan order sensitivity is not well concerned. In this study, we propose a novel Correlation-aware Reordered Scanning Mamba, namely CRS-Mamba, for multivariate time series forecasting. Specifically, we leverage the downsampling technique to model temporal dependencies. Then, a bidirectional Mamba layer is introduced to extract inter-variate correlations. Moreover, we propose Dimensionality Reduction Scan Algorithm to alleviate scanning sensitivity problem of Mamba. Extensive evaluations show that our approach secures superior performance in prediction accuracy on various datasets. Moreover, ablation studies demonstrate the interpretability of CRS-Mamba.