Identifying breakpoints in signals is crucial for uncovering important features in scientific data. In the biomedical field, the heterogeneity of signals leads to increased complexity in identifying breakpoints. While existing methods and software packages most focus on detecting breakpoints in individual signals, a significant challenge in this field is to detect common breakpoints of multiple signals. To address this challenge, a fast and optimal method has been developed and implemented in the R package MSigSeg as a practical tool.The proposed method utilizes an optimization approach with ℓ-0 norm penalty to efficiently and accurately detect the locations of common breakpoints in multiple signals. This article provides a detailed description of the mathematical problem, the fast optimization algorithm which is implemented in the package, and the usage of core functions along with example datasets.To evaluate the performance of the proposed method, a simulation study is conducted, comparing it with other segmentation approaches. Real-world problems such as are also processed to demonstrate the practical value of the package. Substantial efficiency gain can be observed by our results.Our R package MSigSeg implements an efficient and sensitive method for detecting common breakpoints across multiple signals, serving as a valuable resource for the analysis of intricate biomedical signals. The proposed package is available on the Comprehensive R Archive Network (CRAN) repository https://CRAN.R-project.org/package=MSigSeg.