Abstract: The object bundling optimization problem in reverse combinatorial auctions for online procurements and large project tenders is addressed. A mathematical model with multi-objectives is proposed to solve the problem. Because of its nonlinear and non-analytic properties with variable sets, it is very difficult to solve. Thus, a Quantum Evolutionary Algorithm (QEA) is developed for its solution. It adopts the 0-1 encoding scheme with nonzero elements in the cost complementarity matrix, and the β-based rotation gate to enlarge the probability of selecting the better Q-bits. Numerical results from a number of examples show that the β-based rotation gate is evidently better than the α-based one. Compared with a contrastive genetic algorithm, QEA can achieve better computational performances for small- and middle-sized problems.