With the rapid advancement of remote sensing technology, bi-temporal remote sensing change detection (CD) techniques have also seen significant progress. However, existing CD tasks still face two challenges: 1) Variations in lighting and seasonal factors complicate imaging conditions, causing pseudo-variation interference, and 2) The spatial distribution and shapes of building are diverse, leading to difficulties in extracting and utilizing effective change features. In this paper, we propose the spatial-temporal evolution guided change detection network (STEGNet) to capture and fully utilize rich spatial-temporal information. Specifically, we develop the chrono colorizer to mitigate pseudo-variant interference by standardizing color styles and enriching time series information. Additionally, we introduce the temporal-spatial guidance module (T-SGM), which combines spatial-temporal information to guide the decoding operation and mitigate information loss during spatial-temporal fusion, resulting in finer prediction results. Experimental results on three benchmark datasets demonstrate that STEGNet effectively suppresses pseudo-variation interference and significantly improves the integrity of detection boundaries.