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Cold supply chain distribution systems ensure the freshness of temperature-sensitive products during transportation. In this study, we investigated a fresh food company's cold supply chain distribution. Making fresh food available and achieving quality and safety, requires proper planning of vehicle routing, we addressed a routing problem that simultaneously considers time windows, multiple trips per vehicle, a heterogeneous fleet, parking constraints, unloading time at customer position, and limited duration, minimizing related operational costs. We formulate this problem as a mixed-integer programming model. Since this problem is NP-hard, we also propose a genetic algorithm with two adaptive-parameter mechanisms to solve it within a reasonable computational time. Extensive experiments were conducted to assess the performance of different approaches in a real-world application. The results demonstrate that the algorithms are robust and efficient. The proposed algorithms can reduce operational costs by more than 20% compared to the current practical planning approach. [ABSTRACT FROM AUTHOR]