The Travelling Salesman Problem (TSP) involves a salesman travelling to each city and returning to the origin. TSP is used for various types of routing problems; one of them is delivery of perishable products. In this research, a promising metaheuristics algorithm known as the Crow Search Algorithm (CSA) is applied for solving TSP for perishable products. However, most mathematical models do not consider the time constraint aspect of solving problems related to the transshipment of perishable products. Implementation of CSA for the aim of solving TSP appeared to be a difficult task due to a lack in tuning the CSA's parameters. In addition, CSA is not fully applied in the area of routing problems’ research. This research developed a new CSA with combination of additional operators to solve TSP for perishable products in terms of calculating the travel cost and adding time constraint to the model. Besides that, the new CSA performance is verified using benchmark datasets and a simulated case study. The methodology of this research starts by adding a time segregation constraint to the mathematical model of TSP. Then, the implementation of CSA with combination of Neighborhood Moves is introduced to find the solution. The performance and capability of the CSA is coded using MATLAB and tested with 20 instances from TSPLIB. Next, the new CSA is compared with other established results using benchmark datasets which recent and classical algorithms. The findings show promising result by producing 15 instances of TSPLIB less than 10% from the optimal solution. CSA also performed well than classical algorithm where all the results approached the optimal solution. Additionally, when simulated for the case study, CSA is competence to generate the solutions. The impact of time constraint shown positive result in simulated cases that the product reached customer in timely manner. Therefore, CSA has the potential to be explored for future research in various areas such as in vehicle routing problem, capacitated arc routing problem and other variants
