Distributed data acquisition and processing in ubiquitous computing mode have brought the demand for distributed data-driven optimization. To address the challenges such as distributed data acquisition， asynchronous constraints evaluation and incomplete information， a Distributed Data-Driven Evolutionary Algorithm （DDDEA） framework for multi-constrained optimization was constructed. A series of terminal nodes were responsible for data provision and distributed evaluation， while the server nodes were responsible for global evolutionary optimization. Based on this framework， a specific algorithm instance was implemented， the terminal nodes utilized their local data to construct a Radial Basis Function （RBF） model to assist the differential evolution of the server node. Experimental comparison with three centralized data-driven evolutionary algorithms for multi-constrained optimization on two standard test suites show that， the DDDEA achieves significant optimal results in 68.4% of test cases and has a success rate of 1.00 in finding feasible solutions in 84.2% of test cases. Therefore， the DDDEA has satisfactory global search and convergence abilities.