Consecutive traffic signals produce vehicle stops and acceleration/deceleration maneuvers on arterial roads, which may increase vehicle fuel consumption levels significantly. Eco-cooperative adaptive cruise control (Eco-CACC) systems can improve vehicle energy efficiency using connected vehicle (CV) technology. In this thesis, an Eco-CACC system is proposed to compute a fuel-optimized vehicle trajectory while traversing multiple signalized intersections. The proposed system utilizes signal phasing and timing (SPaT) information together with real-time vehicle dynamics data to compute the optimal acceleration/deceleration levels and cruise speeds for connected-technology-equipped vehicles while approaching and leaving signalized intersections, while considering vehicle queues upstream of the intersections. The INTEGRATION microscopic traffic simulation software was used to conduct a comprehensive sensitivity analysis of a set of variables, including different levels of CV market penetration rates (MPRs), demand levels, phase splits, offsets, and distances between intersections to assess the benefits of the proposed algorithm. Based on the analysis, fuel consumption saving increase with an increase in MPRs and a decrease in the cycle length. At a 100% equipped-vehicle MPR, the fuel consumption is reduced by as much as 13.8% relative to the base no Eco-CACC control. The results demonstrate an existence of optimal values for demand levels and the distance between intersections to reach the maximum fuel consumption reduction. Moreover, if the offset is near the optimal values for that specific approach, the benefits from the algorithm are reduced. The algorithm is limited to under-saturated conditions, so the algorithm should be enhanced to deal with over-saturated conditions.