A novel method is proposed for the design of heat exchanger networks (HENs) in non-steady state processes. The first stage involves the application of pinch analysis (PA) using Python code to analyze each individual time interval within the process and manually determine the heat exchanger capacity, identifying the optimal operational domain for each heat exchanger. Then, stream matching leverages these initial designs to determine optimal pairings of hot and cold streams across time intervals, maximizing heat recovery through the identification and matching of heat exchangers with greater heat exchange capacity using a genetic algorithm (GA) in Python. The final stage includes manual modifications to the overall HEN design, adjusting heat exchanger areas, guided by the GA model specifically tailored for non-steady state process optimization. This methodology is validated through a case study focusing on a greenhouse system, demonstrating its efficiency in achieving energy savings and process enhancements.