For most multivariate time series segmentation algorithms, the selection of segmentation points and the determination of the number of segments often need to be completed independently, which greatly increase the computational complexity of the algorithm. In order to solve the above problem, an adaptive greedy Gaussian segmentation algorithm based on multivariate time series is proposed. The algorithm interprets the data points from the segmentations of multivariate time series as independent samples of different multivariate Gaussian distributions, and then transforms the segmentation problem into a covariance-regularized likelihood maximization problem to solve. In order to improve the learning efficiency, the greedy search method is adopted to maximize the likelihood value of each segment to find the optimal segment point approximately. During the search process, the information gain method is adopted to adaptively obtain the optimal number of segments, which avoids from realizing the determination of the number of segments and the selection of segment points independently to reduce the computational complexity. The experimental analysis is carried out on real datasets in many different fields. Compared with traditional methods, the proposed method can obtain higher accuracy and efficiency, and is able to detect outliers in multivariate time series effectively.