The proliferation of the Internet of Things (IoT) and 5G technologies has established the edge-cloud continuum as a key architecture for high-performance, low-latency applications. However, effective task scheduling in these dynamic and heterogeneous environments presents significant challenges, including ensuring multi-hop network reliability under fluctuating conditions and optimizing queue management to prevent computational bottlenecks. To address these issues, this article proposes CPRGO, a novel task scheduling strategy based on an improved plant rhizome growth-based optimization algorithm enhanced with chaotic mapping. CPRGO enhances performance through three key mechanisms: it optimizes the selection of multi-hop transmission paths to boost task success rates, employs task classification and segmentation for more granular scheduling, and introduces a queue reorganization mechanism to mitigate performance degradation. Simulations performed on the RayCloudSim platform demonstrate that CPRGO significantly outperforms the baseline algorithms. It achieves superior performance in task completion rate, average cost, and average delay, demonstrating its robustness and practical applicability.