To address the traffic signal timing plan optimisation problem of mixed traffic flow at adjacent intersections under dynamic traffic demand, the vehicle delay, nonmotor vehicle delay, and pedestrian waiting time are used as optimisation objectives, a dynamic multi-objective optimisation model is constructed, and a multi-population coevolutionary dynamic-multi-objective genetic algorithm (MPCED-MOGA) is proposed, where the population is divided into search and tracking populations. The optimal solution is searched by the search population, while the changes in the environment are tracked by the tracking population. To obtain an algorithm quickly responding to the environment variations, the information is exchanged constantly between the two populations during the evolution. The proposed algorithm is tested under three classical test functions in FDA series of dynamic multi-objective optimisation function. The result is compared to those obtained by three existing dynamic multi-objective optimisation algorithms. The proposed algorithm is advantageous in terms of convergence and distribution. The effectiveness of the proposed algorithm for the signal optimisation problem is evaluated under a simulation environment based on a real adjacent intersection in Shanghai. The proposed MPCED-MOGA reduces the vehicle delay, nonmotor vehicle delay, and pedestrian waiting time compared to those of the three existing dynamic multi-objective optimisation algorithms and dynamic fixed time control method (TRRL).