<p>Scheduling is a fundamental component of dynamic and complex manufacturing systems, coordinating resources efficiently and ensuring timely production. However, designing efficient scheduling rules to maximize delivery performance and resource allocation is challenging due to uncertainty in job arrivals, machine status, and routing changes. Existing Genetic Programming (GP) approaches can automatically evolve scheduling rules but remain limited by their dependence on simulation models, extensive data requirements, and limited adaptability to changing conditions. The goal of this research is to overcome the above challenges by developing the first Online Genetic Programming (OGP) framework that learns scheduling strategies directly within the operating environment and without relying on prior knowledge or an explicit simulation models. The novelty of this research lies in the development of an adaptive fitness function that combines real-time performance feedback with predictive evaluation from a phenotypic archive, allowing the search process to balance short-term adaptability and long-term learning stability. A pre-selection strategy further refines candidate solutions while controlling rule complexity, and a soft restart mechanism sustains diversity during extended evolutionary runs. Dynamic flexible job shop scheduling problems (DFJSP) were used as representative test environments to evaluate the method’s effectiveness. Experimental results on DFJSP demonstrate that OGP outperforms existing scheduling algorithms when jointly considering scheduling and routing decisions. When used as an automated heuristic design method, the proposed method can generate competitive rules compared to the state-of-the-art genetic programming methods in terms of test performance and the size of evolved rules. These findings highlight OGP as a robust and generalisable optimisation framework for dynamic decision-making in changing environments.</p>