Distributed optimization for multiagent systems has attracted much attention on account of its high-efficiency, flexibility and reliability with extensive application prospects in cooperative control of multiple robots, wireless sensor networks, energy systems, etc. The basic goal of distributed optimization is designing a distributed control protocol by utilizing the individual objective function gradient and the state information of the agent and its neighbors to drive the states or outputs of all the agents towards the optimal solution of the global objective function. System dynamics is an important factor to affect the state evolution. Based on reviewing the research results on continuous-time distributed optimization algorithms, a systematic survey on the recent development of distributed optimization for multiagent systems with complex dynamics is conducted according to the categories of system dynamics. The future development directions for the research are also discussed.