process demands that a required quality be maintained at every stage, spanning from the flower growers, wholesalers, and retailers to the end customer. Failure to uphold the required level of freshness can lead to adverse outcomes such as decreased profits, customer attrition, and a tarnished reputation. To investigate this complex distribution process, fresh flower distribution was studied using data from a reputed floral company in Sri Lanka with the aim of finding the optimal set of routes for the distribution of fresh-cut flowers. Methods: Vehicle Routing Problem (VRP) variants were employed and the ‘Capacitated Vehicle Routing Problem with Time Windows for Perishable Goods with Single Depot’ (CVRPTWfPGSD) was introduced. A hybrid method combining both heuristics and metaheuristics was selected as the methodology by considering several factors such as complexity, solution type, and execution time. The initial solution construction phase adopted the Path Cheapest Arc (PCA) heuristic. To further improve solution quality, the Guided Local Search (GLS) metaheuristic was applied. Results: This CVRPTWfPGSD model is validated for real-world scenarios, as it does not violate the maximum allowable perishability time provided for each vehicle. Three experiments were conducted by varying the vehicle fleet to measure the applicability of the model under different circumstances to decide on a better vehicle combination while minimizing total distribution time. Based on this analysis, it can be concluded that the composition of the vehicle fleet has a substantial influence on freshness levels and distribution times. Conclusions: Optimizing the distribution of fresh-cut flowers using VRP assists in reducing spoilage and waste by ensuring that flowers are transported under the best possible conditions. The application of this model holds immense value for floral companies as it offers assistance when planning their distribution network. Specifically, it can assist in identifying optimal routes that maximize freshness with minimum distribution time and an optimal set of vehicles.