Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and conservation science. Several computational algorithms have been developed to model connectivity; however, the major models in use today are limited by their lack of flexibility and simplistic assumptions of movement behaviour. In this paper, we introduce a new spatially-explicit, individual- and process-based model called Pathwalker, which simulates organism movement and connectivity through heterogeneous landscapes as a function of landscape resistance, the energetic cost of movement, mortality risk, autocorrelation, and directional bias towards a destination, all at multiple spatial scales. We describe the model’s structure and parameters and present statistical evaluations to demonstrate the influence of these parameters on the resulting movement patterns. Written in Python 3, Pathwalker works for any version of Python 3 and is freely available to download online. Pathwalker models movement and connectivity with greater flexibility compared with the dominant connectivity algorithms currently available in conservation science, thereby, enabling more detailed predictions for conservation practice and management. Moreover, Pathwalker provides a highly capable simulation framework for exploring theoretical and methodological questions that cannot be addressed with empirical data alone.