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Modern digital platforms rely on related search query recommendations to enhance engagement, yet existing methods fail to reconcile click-through rate (CTR) optimization with topic expansion. We propose **CMAQ**, a **C**onsistent **M**ulti-Objective **A**ligned **Q**uery generation framework that harmonizes these goals through three components: (1) reward modeling to quantify objectives, (2) style alignment for format compliance, and (3) consistency-aware optimization to coordinate joint improvements. CMAQ employs adaptive š¯›½-scaled DPO with geometric mean rewards, balancing CTR and expansion while mitigating objective conflicts. Extensive offline and online evaluations in a large-scale industrial setting demonstrate CMAQā€™s superiority, achieving significant CTR gains (+2.3%) and higher human-rated query quality compared to state-of-the-art methods. Our approach enables high-quality query generation while sustaining user engagement and platform ecosystem health.