Industrial Internet enables distributed manufacturing enterprises to efficiently and promptly respond to the requirements of stakeholders using a manufacturing service collaboration chain (MSCC) composed of networked enterprises. However, various dynamic uncertainties may interrupt the MSCC, such as device malfunctions, urgent order insertions, and dynamic logistics, resulting in inexactitude practical applications. In this article, we propose a novel reliability-based dynamic manufacturing service collaboration optimization (R-DMSCO) model for uncertain manufacturing collaboration procedures on industrial Internet. The R-DMSCO model reformulates the MSCC reliability in the form of an expectation–standard deviation of uncertain job completion time described by discrete scenarios pertaining to the uncertain perturbation of processing time and logistics time. Subsequently, an enhanced multiobjective artificial bee colony (EMOABC) algorithm that embeds four improvements is intended to address the manufacturing service collaboration optimization (MSCO) problem. The experimental results demonstrate that EMOABC outperforms other typical multiobjective algorithms for MSCO problems. Additionally, the R-DMSCO model can cope with dynamic uncertainties with better robustness and stability than two other effective strategies for dynamic manufacturing service collaboration.