Efficient scheduling of water sample analyses is critical in industrial laboratories for directly affecting operational efficiency, resource utilisation and the timely delivery of results. Traditional scheduling approaches like first-in-first-out (FIFO) with urgent sample prioritisation often result in suboptimal resource use and increased delays and over costs. This paper presents a heuristic scheduling algorithm designed to sample sequence by minimising the number of delayed samples, total tardiness, total earliness and operational costs. The developed approach utilises parametrisable input data, such as annual working calendars, machine availability, analytical methods, analysis duration, sample preservation times and equipment calibration costs. The heuristic was validated with historical data from a real-world industrial laboratory by comparing its performance to previous scheduling approaches. The results showed a significant reduction in delayed samples and overall delay, which confirm the algorithm’s potential to dynamically adapt to changing conditions while maintaining optimal sequencing. This study underlines the importance of integrating automated programming methods to improve laboratory efficiency, responsiveness and flexibility.