Earthquake rates are driven by tectonic stress buildup, earthquake‐induced stress changes, and transient aseismic processes. Although the origin of the first two sources is known, transient aseismic processes are more difficult to detect. However, the knowledge of the associated changes of the earthquake activity is of great interest, because it might help identify natural aseismic deformation patterns such as slow‐slip events, as well as the occurrence of induced seismicity related to human activities. For this goal, we develop a Bayesian approach to identify change‐points in seismicity data automatically. Using the Bayes factor, we select a suitable model, estimate possible change‐points, and we additionally use a likelihood ratio test to calculate the significance of the change of the intensity. The approach is extended to spatiotemporal data to detect the area in which the changes occur. The method is first applied to synthetic data showing its capability to detect real change‐points. Finally, we apply this approach to observational data from Oklahoma and observe statistical significant changes of seismicity in space and time.