Abstract: ‍ ‍The airborne distributed multiple-input multiple-output （MIMO） radar system is based on airborne distributed platforms. It adopts multiple radar nodes to simultaneously transmit and receive signals and collaboratively processes multiple radar echoes to improve the signal-to-noise ratio. This improves the surveillance performance of the radar system for the detection area. System resource scheduling is a key technology of airborne distributed MIMO radar systems as it can significantly enhance the utilization rate of node positions， the flight path， and other system resources and increase the ability of target detection. In this paper， a method of node position and path optimization for airborne distributed MIMO radar is proposed. First， based on the radar system’s detection requirements， kinematic constraints， radar node positions， and other factors， the airborne distributed MIMO radar node position and path optimization models are established. Subsequently， particle swarm optimization （PSO） is utilized to optimize the locations of airborne distributed MIMO radar nodes to obtain the optimal location of each radar node. Thereafter， a frame-by-frame path optimization model of multi-aircraft cooperation is established considering the different path-matching criteria of airborne distributed multi-nodes， including the shortest sum of flight paths， the shortest length of the longest flight path， and minimum flight path residuals. The optimal flight paths of different nodes are solved using genetic algorithms （GAs） on a frame-by-frame basis. The simulation results show that compared with the conventional method， the proposed method has better surveillance performance. Compared with the scheme of a straight-line flight path， the flight scheme obtained using the proposed path optimization method can achieve better frame-by-frame surveillance performance.