Accurate dating of landslides is essential for understanding triggering mechanisms and improving hazard analysis, yet many inventories lack precise event timing. This study presents a two-step methodology for dating existing inventories using multi-sensor satellite data and automated change-point detection implemented with the Ruptures Python package. In Step 1, extended time series of Sentinel-2 optical NDVI and the Bare Soil Index are analysed to estimate the approximate dates of landslides. Step 2 refines these estimates using Sentinel-1 SAR VV backscatter data within a six-month window centred on the results from Step 1. The approach is tested using a landslide inventory from Zimbabwe associated with Storm Idai in March 2019. Using the results from Step 2, 52.8 % of the dataset is dated, with 84.6 % accuracy (events correctly dated during the triggering storm event) and an average precision of 12.8 days (the dates between which the event occurred). The results demonstrate that combining optical and SAR satellite observations with automated change-point detection provides an effective method for retroactively dating landslides. This approach enables inventories to be dated with minimal prior knowledge of event timing or geometry, while avoiding the need for large datasets and high-performance computing resources. The code is made available in Google Earth Engine, allowing for wide application.