The purpose of this paper is to develop and study algorithms for group decision making support systems intended for control scheduling of public transport flows on city intersections. The paper regards the problem of visual dynamic modelling and solution of the problem of minimization of energy and transportation costs and time expense of vehicles for the chosen version of the route and schedule at multiple intersections. The work presents research and classification of scheduling theory methods for electromechanical systems control by developed artificial intelligence procedures. Functional dependencies are investigated for electrical processes and system state changing in time. Rules of scheduling theory define time constraints and criteria for optimal control of motion of electromechanical objects and target functions for diagnostics of electrical processes. Mathematical models and procedures are developed for optimal electrical processes’ control performance with scheduling theory rules by electrical energy consumption minimization, process speed maximization and processing time minimization criteria. New algorithm is developed for electric transport flow optimal control. The authors offer a generalized algorithm for solution of the problem of drawing up an optimum schedule for traffic through multiple city intersections with the use of intelligent agents and dynamic computer models of transport flows. It is also supposed that modelling software can detect formation of problematic situations, change the duration of one-type operations on separate processors and interact in search of the best solution. The modelling program is controlling intersections under consideration, while the data collection and analysis program interacts with relational database and makes the optimum processors’ operating schedule with the help of a group decision making support system. The processor is the traffic lights of the intersection, and the operation is handling of a transport flow to an assigned direction and time. With the help of the developed algorithm from a set of electric vehicles for particular intersection applying the Monte-Carlo method we select a certain unit. Its parameters are specified in the model: intersection, flow, vehicle ID, its velocity, acceleration, current power consumption rate, distance to the traffic lights and nearby obstacles, estimated braking distance and the time left until a prohibiting signal of the traffic lights. On the basis of the distance to the traffic lights and obstacles and the braking distance a conclusion on possibility to stop before or on a line at the signal of the traffic lights can be made and thus priority of flows can be adjusted on the spot. Optimum data about the intersection processors’ operating schedule and traffic flow priorities received from the model are passed on by the Software Agents interacting with the database management system, recorded in previously prepared database table and displayed on the screen for users of a group decision making support system.