Optimized scheduling of gasoline production and distribution (SGPD) enables the maximum utilization of resources to reduce the overall operation cost while maintaining the product quality. Moreover, it enables to utilize biofuels and low-quality refinery intermediates which reduces the environmental impact. The problem is complex, non-linear and multi-objective and therefore a more suitable algorithm than the conventional mixed-integer non-linear programming (MINLP) is needed. In this work, a hybrid model which intelligently uses the graphically feasible forms of gasoline production and distribution (GPD) network in continuous time graphical genetic algorithm is developed for both single- and multi-objective optimizations of SGPD. The use of graphically feasible forms of GPD network significantly reduces the number of active constraints and variables that need to be handled in a continuous-time graphical genetic algorithm. In single-criterion optimization, the overall production cost is minimized whereas the variation in inter-event blending rate (a2) is additionally minimized in multi-criterion optimization. The developed approach is used to solve seven industrial problems of varying sizes for which the obtained production cost found to be lower than that obtained using the conventional MINLP approach. Further, the cost reduction is found to be increasing for bigger size problems. Also, the comparison with the discrete-time model shows a significant reduction in active formulation size. (c) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.