This work investigates a bio-inspired immune optimization algorithm in noisy environments, solving a class of chance-constrained programming problems with continuous decision variables but without any a priori distributional information on random variables. In this stochastic optimization method, an efficient adaptive sampling detection scheme is developed to detect individual’s reliability, while those highquality individuals in the current population can be identified based on the reported sample-allocation scheme; a clonal selection-based dynamical evolving mechanism is established to ensure evolving populations strong diversity, noisy suppression and rapidly moving one such population toward the desired region. The comparative experiments show that the proposed algorithm can effectively solve multi-modal chanceconstrained programming problems with high efficiency and is of the potential for engineering application.