Digital　Microfluidic　Biochips　(DMFBs)　represent　a　miniaturized,　integrated,　high-precision,　and　cost-effective　platform　with　widespread　applications　in　fields　such　as　mobile　healthcare.　Nevertheless,　DMFBs　face　increasingly　intricate　advanced　synthesis　challenges.　Furthermore,　the　inclusion　of　unreliable　third-party　elements　in　the　chip　manufacturing　process　and　the　implantation　of　hardware　trojans　can　lead　to　erroneous　measurement　results,　potentially　resulting　in　incorrect　diagnostic　approaches.　This　paper　introduces　a　novel　genetic　algorithm　encoding　approach　to　address　this　issue,　allowing　simultaneous　consideration　of　operation　prioritization,　module　selection,　and　module　placement.　Additionally,　it　defines　three　different　hierarchical　queues　and　employs　distinct　module　selection　strategies　for　each　queue,　enhancing　operational　scheduling　efficiency　and　achieving　higher　spatial　utilization　in　module　placement.　This　approach　outperforms　traditional　module　placement　algorithms,　resulting　in　a　8.7%-34%　improvement　in　execution　time.　Simultaneously,　to　tackle　the　issue　of　incorrect　results　caused　by　the　insertion　of　malicious　hardware　trojans,　the　method　employs　electrode　tagging　to　lower　the　cost　of　DMFB　utilization　while　ensuring　security.　Experimental　results　validate　the　effectiveness　and　security　of　this　approach,　providing　an　essential　foundation　for　trustworthy　advanced　synthesis　design　in　DMFBs.　©　2023　ACM.