因應氣候變遷和能源安全議題，電動車成為世界各國發展趨勢，近年來尤其以大宗運輸應用市場有意圖將傳統燃料車隊轉換成碳排放量極低的電動車隊。本研究主要以城市物流為主軸，和容量限制區位途程問題(Capacitated routing problem, CLR)為雛形，發展一套適用於城市內商業電動車隊配送模式的演算法。本研究方法採用(Harks, König, & Matuschke, 2013)所提出近似演算法為基礎進行修改，並結合背包問題概念，提出一個新的演算方法來克服現實中車輛壅塞情形造成送貨時間延遲及電動車最大哩乘數超過的問題。因此，在每日電動車執行配送任務過程時，除了力求總距離成本最小化外，亦期望能達到任務價值最大化，執行貪婪策略捨棄每趟任務中較低價值顧客措施，來有效提升成本效益。最後，利用新北地區捷盟物流中心配送7-Eleven據點為實例展示演算法的路線規劃成果。;In response to climate change and energy security issues, electric vehicles have become the development trend of countries in the world. In recent years, particularly in the large shipping market–Logistic Industry intended to replace some of the petroleum-based vehicles into electric-based vehicles. In this study, Considering the Capacitates Location Problem (CLR) is defined and formulated for performing distribution task in city logistic, develop a new model suitable inner city can be simulated used by commercial electric fleet. Methods in the study modified based on approximation algorithms proposed by be Harks, König, & Matuschke (2013) as a prototype, and combined with the concept of knapsack problem. The new method can overcome when the vehicle delay the delivery time because of traffic congestion. Therefore, the study in the performing of distribution daily task process not only seeks for minimize-cost routes, but also expects to achieve value maximization task. Executing greedy strategy that is abandon the lower value of the customer measures could effectively improve cost-effectiveness. Finally, the paper takes the 7-Eleven in New Taipei city, Taiwan as a study case to demonstrate the result of the present algorithm.