Increasing　urban　traffic　congestion　and　environmental　pollution　have　led　to　the　embrace　of　bike-sharing　for　its　low-carbon　convenience.　This　study　enhances　the　operational　efficiency　and　environmental　benefits　of　bike-sharing　systems　by　optimizing　electronic　fences　(e-fences).　Using　bike-sharing　order　data　from　Shenzhen,　China,　a　data-driven　multi-objective　optimization　approach　is　proposed　to　design　the　sustainable　dynamic　capacity　of　e-fences.　A　dynamic　planning　model,　solved　with　an　improved　Non-dominated　Sorting　Genetic　Algorithm　II　(NSGA-II),　adjusts　e-fence　capacities　to　match　fluctuating　user　demand,　optimizing　resource　utilization.　The　results　show　that　an　initial　placement　of　20　bicycles　per　e-fence　provided　a　balance　between　cost　efficiency　and　user　convenience,　with　the　enterprise　cost　being　approximately　76,000　CNY　and　an　extra　walking　distance　for　users　of　15.1　m.　The　optimal　number　of　e-fence　sites　was　determined　to　be　40　based　on　the　solution　algorithm　constructed　in　the　study.　These　sites　are　strategically　located　in　high-demand　areas,　such　as　residential　zones,　commercial　districts,　educational　institutions,　subway　stations,　and　parks.　This　strategic　placement　enhances　urban　mobility　and　reduces　disorderly　parking.