Task scheduling is a significant challenge within the field of artificial intelligence due to its relevance to many areas such as robotics, logistics, and workflow management. The current paper describes the building of Stanford Research Institute Planning System (STRIPS) based planner for the purposes of task scheduling. Planning as a concept consists of actions or tasks that have a defined precondition and an effect, thus, making it possible to break down any complex scheduling problems into simpler multi-level ones. The first step is to transform the practical scheduling problems into STRIPS task geometrical structure, showing resource constraints, tasks relationships and the objective. The planner then produces a series of actions that meet the stated objectives within the given constraints in the most efficient manner. Evidence shows that the STRIPS planner is capable of handling scheduling problems and it is also efficient in terms of varying the size and complexity of the problems that it can solve.This study provides evidence for the use of STRIPS-type planners in intelligent automation, which is the missing link between planning decomposition concepts and the available equipment. The results offer positive perspectives for future research on enhancing the efficiency of the planning process and adapting the model for dynamic and probabilistic scheduling problems.