Coal to ethylene glycol still lacks algorithm optimization achievements for distillation sequencing due to high-dimension and strong nonconvexity characteristics, although there are numerous reports on horizontal comparisons and process revamping. This scenario triggers the navigation in this paper into the simultaneous optimization of parameters and heat integration of the coal to ethylene glycol distillation scheme and double-effect superstructure by the self-adapting dynamic differential evolution algorithm. To mitigate the influence of the strong nonconvexity, a redistribution strategy is adopted that forcibly expands the population search domain by exerting external influence and then shrinks it again to judge the global optimal solution. After two redistributive operations under the parallel framework, the total annual cost and CO₂ emissions are 0.61%/1.85% better for the optimized process and 3.74%/14.84% better for the superstructure than the sequential optimization. However, the thermodynamic efficiency of sequential optimization is 11.63% and 10.34% higher than that of simultaneous optimization. This study discloses the unexpected great energy-saving potential for the coal to ethylene glycol process that has long been unknown, as well as the strong ability of the self-adapting dynamic differential evolution algorithm to optimize processes described by the high-dimensional mathematical model.